{"id":42527,"url":"https://github.com/willxxy/awesome-mmps","name":"awesome-mmps","description":"Corpus of resources for multimodal machine learning with physiological signals (mmps).","projects_count":446,"last_synced_at":"2026-10-18T16:00:19.471Z","repository":{"id":190875273,"uuid":"683509709","full_name":"willxxy/awesome-mmps","owner":"willxxy","description":"Corpus of resources for multimodal machine learning with physiological signals (mmps).","archived":false,"fork":false,"pushed_at":"2026-07-23T16:01:46.000Z","size":1281,"stargazers_count":166,"open_issues_count":0,"forks_count":10,"subscribers_count":10,"default_branch":"main","last_synced_at":"2026-10-10T17:35:39.700Z","etag":null,"topics":["biosignals","deep-learning","machine-learning","multimodal","multimodal-data","multimodal-deep-learning","multimodal-learning","physiological-signals","signal-processing","wearable","wearable-devices"],"latest_commit_sha":null,"homepage":"","language":null,"has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"mit","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/willxxy.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":"CITATION.cff","codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null,"zenodo":null,"notice":null,"maintainers":null,"copyright":null,"agents":null,"claude":null,"gemini":null,"cursor":null,"copilot":null,"dco":null,"cla":null,"disclosure":null}},"created_at":"2023-08-26T19:50:03.000Z","updated_at":"2026-09-23T06:48:10.000Z","dependencies_parsed_at":"2026-08-29T00:44:22.559Z","dependency_job_id":null,"html_url":"https://github.com/willxxy/awesome-mmps","commit_stats":null,"previous_names":["willxxy/awesome-mmps"],"tags_count":2,"template":false,"template_full_name":null,"purl":"pkg:github/willxxy/awesome-mmps","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/willxxy%2Fawesome-mmps","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/willxxy%2Fawesome-mmps/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/willxxy%2Fawesome-mmps/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/willxxy%2Fawesome-mmps/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/willxxy","download_url":"https://codeload.github.com/willxxy/awesome-mmps/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/willxxy%2Fawesome-mmps/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":344872684,"owners_count":38338311,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-10-03T21:59:58.778Z","status":"online","status_checked_at":"2026-10-18T02:00:06.448Z","response_time":63,"last_error":null,"robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":true,"can_crawl_api":true,"host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"created_at":"2024-01-13T21:18:23.806Z","updated_at":"2026-10-18T16:00:19.472Z","primary_language":null,"list_of_lists":false,"displayable":true,"categories":["Laboratories","Publications-and-Preprints","Datasets"],"sub_categories":["Other :placard: + X","EEG :brain: + X","EOG :eye: + X","ECG :anatomical_heart: + X","EDA :sweat_drops: + X","Eye Movement :eye: + X","EMG :muscle: + X"],"readme":"# awesome-mmps\n[![DOI](https://zenodo.org/badge/683509709.svg)](https://doi.org/10.5281/zenodo.10903466)\n\nCorpus of resources for multimodal machine learning with physiological signals.\n\nAny additions, corrections, or concerns please submit an issue. For additions to the list, please provide the relevant information. Thank you! :)\n\n***\n\n## Table of Contents\n\n- [Publications and Preprints](#publications-and-preprints)\n  - [EEG :brain: + X](#eeg-brain--x)\n  - [ECG :anatomical_heart: + X](#ecg-anatomical_heart--x)\n  - [EDA :sweat_drops: + X](#eda-sweat_drops--x)\n  - [Eye Movement :eye: + X](#eye-movement-eye--x)\n  - [EOG :eye: + X](#eog-eye--x)\n  - [EMG :muscle: + X](#emg-muscle--x)\n  - [Other :placard: + X](#other-placard--x)\n- [Datasets](#datasets)\n\n- [Laboratories](#laboratories)\n\n- [Citation](#citation)\n\n\n## Publications-and-Preprints\n\n### EEG :brain: + X \n- [Comparing Recognition Performance and Robustness of Multimodal Deep Learning Models for Multimodal Emotion Recognition](https://ieeexplore.ieee.org/abstract/document/9395500), IEEE TCDS 2021; [EEG, Eye Movement, Peripheral Physiological Signals, ECG]\n- [Multi-view Emotion Recognition Using Deep Canonical Correlation Analysis](https://bcmi.sjtu.edu.cn/~blu/papers/2018/Qiu2018Multi-viewEmotion.pdf), ICONIP 2018; [EEG, Eye Movement]\n- [Correlated Attention Networks for Multimodal Emotion Recognition](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=8621129), IEEE BIBM 2018; [EEG, Eye Movement]\n- [Investigating Sex Differences in Classification of Five Emotions from EEG and Eye Movement Signals](https://drive.google.com/file/d/1pCln0DI3fiIPHHiRiCRcXJWb4LCWskXm/view), IEEE EMBC 2019; [EEG, Eye Movement]\n- [Can Brain Signals Reveal Inner Alignment with Human Languages?](https://arxiv.org/pdf/2208.06348.pdf), EMNLP Findings 2023; [EEG, Text]\n- [Open Vocabulary Electroencephalography-to-Text Decoding and Zero-Shot Sentiment Classification](https://arxiv.org/abs/2112.02690), AAAI 2022; [EEG, Text]\n- [Decoding EEG Brain Activity for Multi-Modal Natural Language Processing](https://www.frontiersin.org/articles/10.3389/fnhum.2021.659410/full?\u0026utm_source=Email_to_authors_\u0026utm_medium=Email\u0026utm_content=T1_11.5e1_author\u0026utm_campaign=Email_publication\u0026field=\u0026journalName=Frontiers_in_Human_Neuroscience\u0026id=659410), Frontiers in Human Neuroscience 2021; [EEG, Eye Movement, Text]\n- [Advancing NLP with Cognitive Language Processing Signals](https://arxiv.org/pdf/1904.02682.pdf), arxiv 2019; [EEG, Text, Gaze]\n- [Recognition of emotions using multimodal physiological signals and an ensemble deep learning model](https://www.sciencedirect.com/science/article/pii/S0169260716305090), Computer Methods and Programs in Biomedicine 2017; [EEG, Peripheral Physiological Signals]\n- [Multi-modal emotion analysis from facial expressions and electroencephalogram](https://www.sciencedirect.com/science/article/pii/S1077314215002106), Computer Vision and Image Understanding 2016; [EEG, Facial Expressions]\n- [An Ensemble Learning Method for Emotion Charting Using Multimodal Physiological Signals](https://www.mdpi.com/1424-8220/22/23/9480), Sensors 2022; [EEG, ECG, GSR]\n- [Emotion recognition based on multi-modal physiological signals and transfer learning](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9493208/), Frontiers in Neuroscience 2022; [EEG, RSP, GSR, Photo-Plethysmograph]\n- [Multimodal Affective States Recognition Based on Multiscale CNNs and Biologically Inspired Decision Fusion Model](https://arxiv.org/abs/1911.12918), IEEE Transactions on Affective Computing 2023; [EEG, Peripheral Physiological Signals]\n- [A Multimodal Approach to Estimating Vigilance Using EEG and Forehead EOG](https://iopscience.iop.org/article/10.1088/1741-2552/aa5a98/pdf), Journal of Neural Engineering 2017; [EEG, EOG]\n- [Multimodal Emotion Recognition Using Deep Neural Networks](https://bcmi.sjtu.edu.cn/~liuwei/Wei%20Liu's%20HomePage_files/th_paper_2017.pdf), ICONIP 2017; [EEG, Eye Movement]\n- [EmotionMeter: A Multimodal Framework for Recognizing Human Emotions](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=\u0026arnumber=8283814), IEEE Transactions on Cybernetics 2019; [EEG, Eye Movement]\n- [A Deep Learning Architecture for Temporal Sleep Stage Classification Using Multivariate and Multimodal Time Series](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=8307462), IEEE Transactions on Neural Systems and Rehabilitation Engineering 2018; [EEG, EOG, EMG]\n- [Multimodal Adaptive Emotion Transformer with Flexible Modality Inputs on A Novel Dataset with Continuous Labels](https://www.semanticscholar.org/paper/Multimodal-Adaptive-Emotion-Transformer-with-Inputs-Jiang-Liu/a08b89ea7269be93bbe0d7f5f8493471b4d1c39c), ACM 2023; [EEG, Eye Movement]\n- [Emotion Transformer Fusion: Complementary Representation Properties of EEG and Eye Movements on Recognizing Anger and Surprise](https://bcmi.sjtu.edu.cn/~lubaoliang/papers/2021/2021-14.pdf), IEEE BIBM 2023; [EEG, Eye Movement]\n- [Multimodal Physiological Signals Fusion for Online Emotion Recognition](https://dl.acm.org/doi/pdf/10.1145/3581783.3612555), ACM Multimedia 2023; [EEG, ECG, GSR]\n- [Graph to Grid: Learning Deep Representations for Multimodal Emotion Recognition](https://dl.acm.org/doi/pdf/10.1145/3581783.3612074), ACM Multimedia 2023; [EEG, Eye Movement, GSR, Respiration, ECG]\n- [Stress Classification by Multimodal Physiological Signals Using Variational Mode Decomposition and Machine Learning](https://www.semanticscholar.org/reader/f23f2f2a8bd3f80b44042fd5b58c57c76fcdf281), Hindawi Journal of Healthcare Engineering 2021; [ECG, EEG]\n- [Language Mapping using tEEG and EEG Data with Convolutional Neural Networks](https://www.semanticscholar.org/paper/Language-Mapping-using-tEEG-and-EEG-Data-with-Adhikari-Pham/c960eee117d06c20bb5c9a54aedb707a8c277289), IEEE 2022; [EEG, tEEG]\n- [Understanding language-elicited EEG data by predicting it from a fine-tuned language model](https://www.semanticscholar.org/paper/Understanding-language-elicited-EEG-data-by-it-from-Schwartz-Mitchell/dacd64e55bea910486289ceac2a5f94217433b79), NAACL 2019; [EEG, ERP, Text]\n- [Validation and Interpretation of a Multimodal Drowsiness Detection System Using Explainable Machine Learning](https://www.sciencedirect.com/science/article/pii/S0169260723005916), Computer Methods and Programs in Biomedicine 2023; [EEG, ECG, EOG]\n- [A Classification Model for Sensing Human Trust in Machines Using EEG and GSR](https://dl.acm.org/doi/pdf/10.1145/3132743), ACM Transactions on Interactive Intelligent Systems 2018; [EEG, GSR]\n- [Evaluating the Stressful Commutes Using Physiological Signals and Machine Learning Techniques](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=\u0026arnumber=9990849), IEEE 3ICT 2022; [HR, BP, EEG]\n- [Review of Machine and Deep Learning Techniques in Epileptic Seizure Detection using Physiological Signals and Sentiment Analysis](https://dl.acm.org/doi/pdf/10.1145/3552512), ACM 2022; [iEEG, EEG]\n- [ECG and EEG based detection and multilevel classification of stress using machine learning for specified genders: A preliminary study](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10473514/), PLoS One 2023; [EEG, ECG]\n- [Modeling Multivariate Biosignals With Graph Neural Networks and Structured State Space Models](https://proceedings.mlr.press/v209/tang23a.html), CHIL 2023; [EEG, ECG, PSG]\n- [Cognitive Load Prediction from Multimodal Physiological Signals using Multiview Learning.](https://www.semanticscholar.org/paper/Cognitive-Load-Prediction-from-Multimodal-Signals-Liu-Yu/5103ff5e420ff93bd37ff414e9789fda99e46424), IEEE journal of biomedical and health informatics 2023; [EEG, EDA, ECG, EOG, \u0026 Eye Movements]\n- [An Efficient Multimodal Emotion Identification Using FOX Optimized Double Deep Q-Learning](https://dl.acm.org/doi/10.1007/s11277-023-10685-w), Wireless Personal Communications 2023; [GSR, ECG, and EEG]\n- [EEG2TEXT: Open Vocabulary EEG-to-Text Decoding with EEG Pre-Training and Multi-View Transformer](https://arxiv.org/abs/2405.02165), arxiv 2024; [EEG, Text]\n- [Are EEG-to-Text Models Working?](https://arxiv.org/abs/2405.06459), arxiv 2024; [EEG, Text]\n- [Aligning Semantic in Brain and Language: A Curriculum Contrastive Method for Electroencephalography-to-Text Generation](https://pubmed.ncbi.nlm.nih.gov/37698960/), IEEE Transactions on Neural Systems and Rehabilitation Engineering 2023, [EEG, Text]\n- [A Multitask Framework for Emotion Recognition Using EEG and Eye Movement Signals with Adversarial Training and Attention Mechanism](https://ieeexplore.ieee.org/abstract/document/10385505), IEEE BIBM 2023; [EEG, Eye Movement]\n- [Temporal-Spatial Prediction: Pre-Training on Diverse Datasets for EEG Classification](https://ieeexplore.ieee.org/abstract/document/10447845), ICASSP 2024; [EEG]\n- [Joint Contrastive Learning with Feature Alignment for Cross-Corpus EEG-based Emotion Recognition](https://arxiv.org/abs/2404.09559), arxiv 2024; [EEG]\n- [EEGFormer: Towards Transferable and Interpretable Large-Scale EEG Foundation Model](https://arxiv.org/abs/2401.10278), arxiv 2024; [EEG]\n- [AC-CfC: An attention-based convolutional closed-form continuous-time neural network for raw multi-channel EEG-based emotion recognition](https://www.sciencedirect.com/science/article/pii/S1746809424003070), Biomedical Signal Processing and Control 2024; [EEG]\n- [EEG-GPT: Exploring Capabilities of Large Language Models for EEG Classification and Interpretation](https://arxiv.org/abs/2401.18006), arxiv 2024; [EEG, Text]\n- [Large Brain Model for Learning Generic Representations with Tremendous EEG Data in BCI](https://openreview.net/forum?id=QzTpTRVtrP), ICLR 2024; [EEG, Text]\n- [Naturalistic Emotion Recognition Using EEG and Eye Movements](https://bcmi.sjtu.edu.cn/home/blu/papers/2023/2023-8.pdf), Springer Nature Singapore 2023; [EEG, Eye Movement]\n- [EmotionKD: A Cross-Modal Knowledge Distillation Framework for Emotion Recognition Based on Physiological Signals](https://dl.acm.org/doi/pdf/10.1145/3581783.3612277), ACM 2023; [EEG, GSR]\n- [Negative emotion recognition using multimodal physiological signals for advanced driver assistance systems](https://link.springer.com/article/10.1007/s10015-023-00858-y), Artificial Life and Robotics 2023; [ECG, EDA, EEG, and fNIRS]\n- [Physiological Fusion Net: Quantifying Individual VR Sickness with Content Stimulus and Physiological Response](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=8802983), IEEE International Conference on Image Processing (ICIP) 2019; [Vision, EEG, ECG, GSR]\n- [DeWave: Discrete EEG Waves Encoding for Brain Dynamics to Text Translation](https://arxiv.org/abs/2309.14030), Neurips 2023; [EEG, Text]\n- [DreamDiffusion: Generating High-Quality Images from Brain EEG Signals](https://arxiv.org/pdf/2306.16934.pdf), arxiv 2023; [EEG, Vision]\n- [Multi-Signal Reconstruction Using Masked Autoencoder From EEG During Polysomnography](https://arxiv.org/abs/2311.07868), IEEE BCI 2023; [EEG, EOG, Chin EMG, Event Makers]\n- [Multimodal Physiological Signal Emotion Recognition Based on Convolutional Recurrent Neural Network](https://iopscience.iop.org/article/10.1088/1757-899X/782/3/032005), IOP 2020; [EOG, EMG, GSR, RSP, BVP, TMP, EEG]\n- [Comparative analysis of physiological signals and electroencephalogram (EEG) for multimodal emotion recognition using generative models](https://ieeexplore.ieee.org/abstract/document/7010181), IEEE STSIVA 2014; [EEG, EOG, EMG, GSR, HR]\n- [Large Language Models for Time Series: A Survey](https://arxiv.org/pdf/2402.01801.pdf), arxiv 2024; [EEG, ECG, Text, Vision]\n- [An Exploratory Study of Multimodal Physiological Data in Jazz Improvisation Using Basic Machine Learning Techniques](https://arxiv.org/ftp/arxiv/papers/2401/2401.12266.pdf), arxiv 2024; [EDA, EEG]\n- [Multimodal Multi-View Spectral-Spatial-Temporal Masked Autoencoder for Self-Supervised Emotion Recognition](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10447194), ICASSP 2024; [EEG, Eye Movement]\n- [Functional Emotion Transformer for EEG-Assisted Cross-Modal Emotion Recognition](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10446937), ICASSP 2024; [EEG, Eye Movement]\n- [EEG-Transformer: Self-attention from transformer architecture for decoding EEG of imagined speech](https://ieeexplore.ieee.org/abstract/document/9735124), IEEE BCI 2022; [EEG, Speech]\n- [Towards Voice Reconstruction from EEG during Imagined Speech](https://ojs.aaai.org/index.php/AAAI/article/view/25745), AAAI 2023; [EEG, Speech]\n- [Toward Fully-End-to-End Listened Speech Decoding from EEG Signals](https://arxiv.org/abs/2406.08644), InterSpeech 2024; [EEG, Speech]\n- [Enhancing Affective Representations Of Music-Induced Eeg Through Multimodal Supervision And Latent Domain Adaptation](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=\u0026arnumber=9746643), ICASSP 2022; [EEG, Audio]\n- [Scaling Law in Neural Data: Non-Invasive Speech Decoding with 175 Hours of EEG Data](https://arxiv.org/abs/2407.07595), arxiv 2024; [EEG, Speech]\n- [Hypercomplex Multimodal Emotion Recognition from EEG and Peripheral Physiological Signals](https://arxiv.org/abs/2310.07648), IEEE ICASSP Workshops 2023; [EEG, ECG, GSR]\n- [EEG-Based Multimodal Emotion Recognition: A Machine Learning Perspective](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=\u0026arnumber=10443946), IEEE Transactions on Instrumentation and Measurement 2024; [EEG, ECG, EMG]\n- [SleepFM: Multi-modal Representation Learning for Sleep Across Brain Activity, ECG and Respiratory Signals](https://raw.githubusercontent.com/mlresearch/v235/main/assets/thapa24a/thapa24a.pdf), ICML 2024; [ECG, EEG, EOG, EMG, Respiratory Sensors]\n- [A generative foundation model for five-class sleep staging with arbitrary sensor input](https://www.arxiv.org/abs/2408.15253), arxiv 2024; [EEG, EOG, EMG]\n- [NeuroLM: A Universal Multi-task Foundation Model for Bridging the Gap between Language and EEG Signals](https://arxiv.org/abs/2409.00101), arxiv 2024; [EEG, Text]\n- [Explainable Artificial Intelligence on Biosignals for Clinical Decision Support](https://dl.acm.org/doi/pdf/10.1145/3637528.3671459), KDD 2024; [EEG, ECG]\n- [Recent Trends of Multimodal Affective Computing: A Survey from NLP Perspective](https://arxiv.org/pdf/2409.07388), arxiv 2024; [EEG, ECG, Audio, Text, Image]\n- [EEG-Language Modeling for Pathology Detection](https://arxiv.org/pdf/2409.07480), arxiv 2024; [EEG, Text]\n- [An ASR-based Hybrid Approach for Auditory Attention Decoding](https://lup.lub.lu.se/luur/download?func=downloadFile\u0026recordOId=9173483\u0026fileOId=9173484), Lund University Publications 2024; [EEG, Audio]\n- [SEE: Semantically Aligned EEG-to-Text Translation](https://arxiv.org/pdf/2409.16312), arxiv 2024; [EEG, Text]\n- [Neural Speech Tracking in EEG: Integrating Acoustics and Linguistics for Hearing Aid Users](https://lup.lub.lu.se/luur/download?func=downloadFile\u0026recordOId=9174277\u0026fileOId=9174278), Lund University Publications 2024; [EEG, Audio]\n- [LLM-enhanced Multi-teacher Knowledge Distillation for Modality-Incomplete Emotion Recognition in Daily Healthcare](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=\u0026arnumber=10697478), IEEE Journal of Biomedical and Health Informatics 2024; [EEG, GSR]\n- [Nested Deep Learning Model Towards A Foundation Model for Brain Signal Data](https://arxiv.org/abs/2410.03191), arxiv 2024; [EEG, MEG]\n- [EEG Emotion Copilot: Pruning LLMs for Emotional EEG Interpretation with Assisted Medical Record Generation](https://arxiv.org/abs/2410.00166), arxiv 2024; [EEG, Text]\n- [Thought2Text: Text Generation from EEG Signal using Large Language Models (LLMs)](https://arxiv.org/pdf/2410.07507), arxiv 2024; [EEG, Text]\n- [EEGPT: Unleashing the Potential of EEG Generalist Foundation Model by Autoregressive Pre-training](https://openreview.net/forum?id=wJ6Bx1IYrQ), OpenReview 2024; [EEG, Text]\n- [ARIEL: Brain-Computer Interfaces meet Large Language Models for Emotional Support Conversation](https://dl.acm.org/doi/10.1145/3631700.3665193), Proceedings of the 32nd ACM Conference on User Modeling, Adaptation and Personalization 2024; [EEG, Text]\n- [Promoting cross-modal representations to improve multimodal foundation models for physiological signals](https://arxiv.org/pdf/2410.16424), Medical Foundation Models (AIM-FM) workshop at NeurIPS 2024; [EEG, EMG, EOG, ECG]\n- [DPD (DePression Detection) Net: a deep neural network for multimodal depression detection](https://link.springer.com/article/10.1007/s13755-024-00311-9), Health Information Science and Systems 2024; [EEG, Speech]\n- [Deep-learning models reveal how context and listener attention shape electrophysiological correlates of speech-to-language transformation](https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1012537), PLOS Computational Biology 2024; [EEG, Speech]\n- [Learning under Label Noise through Few-Shot Human-in-the-Loop Refinement](https://arxiv.org/pdf/2401.14107), arxiv 2024; [EEG, IMU, EMG, EOG, ECG]\n- [Position: Addressing Ethical Challenges and Safety Risks in GenAI-Powered Brain-Computer Interfaces](https://www.researchgate.net/profile/Konstantinos-Barmpas-2/publication/385781911_Position_Addressing_Ethical_Challenges_and_Safety_Risks_in_GenAI-Powered_Brain-Computer_Interfaces/links/6735046ca78ba469f060b48a/Position-Addressing-Ethical-Challenges-and-Safety-Risks-in-GenAI-Powered-Brain-Computer-Interfaces.pdf), GenAI for Health Workshop @ NeurIPS 2024; [EEG, Text]\n- [A Hybrid Artificial Intelligence System for Automated EEG Background Analysis and Report Generation](https://ieeexplore.ieee.org/abstract/document/10752384), IEEE Journal of Biomedical and Health Informatics 2024; [EEG, Text]\n- [PedSleepMAE: Generative Model for Multimodal Pediatric Sleep Signals](https://arxiv.org/pdf/2411.00718), IEEE International Conference on Biomedical and Health Informatics 2024; [EEG, EMG, EOG]\n- [Decoding text from electroencephalography signals: A novel Hierarchical Gated Recurrent Unit with Masked Residual Attention Mechanism](https://www.sciencedirect.com/science/article/pii/S0952197624017731), Engineering Applications of Artificial Intelligence 2024; [EEG, Text]\n- [Towards Neural Foundation Models for Vision: Aligning EEG, MEG, and fMRI Representations for Decoding, Encoding, and Modality Conversion](https://arxiv.org/pdf/2411.09723), arxiv 2024; [EEG, fMRI, MEG, Image]\n- [A Survey of LLMs on Biosignal Applications](https://d197for5662m48.cloudfront.net/documents/publicationstatus/232019/preprint_pdf/a5e9124b3b3daff3a6e7766c3e57f047.pdf), arxiv 2024; [ECG, PPG, EEG, IMU, Text]\n- [A novel multimodal EEG-image fusion approach for emotion recognition: introducing a multimodal KMED dataset](https://link.springer.com/article/10.1007/s00521-024-10925-5), Neural Computing and Applications 2024; [EEG, Image, Video]\n- [Representation of Articulatory Features in EEG During Speech Production Tasks](https://ieeexplore.ieee.org/abstract/document/10800095), International Symposium on Chinese Spoken Language Processing 2024; [EEG, Speech]\n- [Decoding individual words from non-invasive brain recordings across 723 participants], arxiv 2024; [EEG, MEG, Text]\n- [From EEG to Eye Movements: Cross-modal Emotion Recognition Using Constrained Adversarial Network with Dual Attention](https://ieeexplore.ieee.org/abstract/document/10818647), IEEE Transactions on Affective Computing 2024; [EEG, Eye Movement]\n- [Brain-model neural similarity reveals abstractive summarization performance](https://www.nature.com/articles/s41598-024-84530-w), Nature Scientific Reports 2025; [EEG, Text]\n- [Develop an emotion recognition system using jointly connectivity between electroencephalogram and electrocardiogram signals](https://www.cell.com/heliyon/fulltext/S2405-8440(25)00147-1), Heliyon CellPress 2025; [ECG, EEG]\n- [On Creating A Brain-To-Text Decoder](https://arxiv.org/pdf/2501.06326), arxiv 2025; [EEG, Text]\n- [Cross-modality fusion with EEG and text for enhanced emotion detection in English writing](https://www.proquest.com/docview/3156655426?pq-origsite=gscholar\u0026fromopenview=true\u0026sourcetype=Scholarly%20Journals), Frontiers in Neurorobot 2025; [EEG, Text]\n- [Decoding Neural Signatures of Language Comprehension and Production through EEG based Brain-Computer Interfaces](http://essay.utwente.nl/104958/1/Krishnaswamynathan_MSc_EEMCS.pdf), preprint 2025; [EEG, Text]\n- [Neural Spelling: A Spell-Based BCI System for Language Neural Decoding](https://arxiv.org/pdf/2501.17489), arxiv 2025; [EEG, Text]\n- [Multi-branch convolutional neural network with cross-attention mechanism for emotion recognition](https://www.nature.com/articles/s41598-025-88248-1), Nature Scientific Reports 2025; [EEG, Text]\n- [A Multimodal Sleep Foundation Model Developed with 500K Hours of Sleep Recordings for Disease Predictions](https://www.medrxiv.org/content/10.1101/2025.02.04.25321675v1.full.pdf), medRxiv 2025; [EEG, EOG, EMG, ECG, Respiratory]\n- [Multimodal Physiological Signals Representation Learning via Multiscale Contrasting for Depression Recognition](https://arxiv.org/pdf/2406.16968), Proceedings of the 32nd ACM International Conference on Multimedia 2024; [EEG, fNIRS]\n- [Finetuning and Quantization of EEG-Based Foundational BioSignal Models on ECG and PPG Data for Blood Pressure Estimation](https://www.arxiv.org/abs/2502.17460), arxiv 2025; [ECG, PPG, EEG]\n- [Brain-to-Text Decoding: A Non-invasive Approach via Typing](https://arxiv.org/pdf/2502.17480), arxiv 2025; [EEG, MEG, Text]\n- [Bridging Brain Signals and Language: A Deep Learning Approach to EEG-to-Text Decoding](https://arxiv.org/pdf/2502.17465), arxiv 2025; [EEG, Text]\n- [Data Foundations for Large Scale Multimodal Clinical Foundation Models](https://www.arxiv.org/abs/2503.07667), arxiv 2025; [EEG, ECG, Text, Image, Video]\n- [Integrated Word2Vec-Based Speech Annotations for Enhanced EEG Decoding of Speech Intentions](https://ieeexplore.ieee.org/abstract/document/10931508), IEEE International Winter Workshop on Brain-Computer Interface (BCI) 2025; [EEG, Text]\n- [Towards Robust Multimodal Physiological Foundation Models: Handling Arbitrary Missing Modalities](https://arxiv.org/abs/2504.19596), arxiv 2025; [EEG, ECG, EMG, EOG]\n- [Robust Sleep Staging over Incomplete Multimodal Physiological Signals via Contrastive Imagination](https://papers.nips.cc/paper_files/paper/2024/file/cb0f9020c00fc52a9f6c9dbfacc6ac58-Paper-Conference.pdf), NeurIPS 2024; [EEG, EMG, EOG]\n- [Pretraining Large Brain Language Model for Active BCI: Silent Speech](https://arxiv.org/pdf/2504.21214), arxiv 2025; [EEG, Text]\n- [EEG-to-Text Translation: A Model for Deciphering Human Brain Activity](https://arxiv.org/abs/2505.13936), arxiv 2025; [EEG, Text]\n- [Learning Interpretable Representations Leads to Semantically Faithful EEG-to-Text Generation](https://www.arxiv.org/abs/2505.17099), arxiv 2025; [EEG, Text]\n- [Recognizing Natural Images From EEG With Language-Guided Contrastive Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=11015337), IEEE Transactions on Neural Networks and Learning Systems 2025; [EEG, Text, Image]\n- [Neural-MCRL: Neural Multimodal Contrastive Representation Learning for EEG-based Visual Decoding](https://arxiv.org/abs/2412.17337v2), arxiv 2025; [EEG, Image, Text]\n- [BrainOmni: A Brain Foundation Model for Unified EEG and MEG Signals](https://arxiv.org/pdf/2505.18185), arxiv 2025; [EEG, MEG]\n- [EEG2TEXT-CN: An Exploratory Study of Open-Vocabulary Chinese Text-EEG Alignment via Large Language Model and Contrastive Learning on ChineseEEG](https://arxiv.org/abs/2506.00854), arxiv 2025; [EEG, Text]\n- [A Pre-trained Framework for Multilingual Brain Decoding Using Non-invasive Recordings](https://www.arxiv.org/abs/2506.03214), arxiv 2025; [EEG, Text]\n- [Bridging Brain with Foundation Models through Self-Supervised Learning](https://arxiv.org/abs/2506.16009), arxiv 2025; [EEG, MEG, fMRI, fNIRS, ECoG, Intracortical]\n- [Efficient Personalized Adaptation for Physiological Signal Foundation Model](https://openreview.net/forum?id=55ysNwbOTI), ICML 2025; [EEG, EOG, EMG, ECG]\n- [EEG-Language Pretraining for Highly Label-Efficient Clinical Phenotyping](https://openreview.net/forum?id=yaI2ZYFmeD), ICML 2025; [EEG, Text]\n- [UniMind: Unleashing the Power of LLMs for Unified Multi-Task Brain Decoding](https://arxiv.org/pdf/2506.18962), arxiv 2025; [EEG, Text]\n- [Exploring the Potential of Electroencephalography Signal–Based Image Generation Using Diffusion Models: Integrative Framework Combining Mixed Methods and Multimodal Analysis](https://medinform.jmir.org/2025/1/e72027/), JMIR 2025; [EEG, Image]\n- [Interpretable EEG-to-Image Generation with Semantic Prompts](https://arxiv.org/abs/2507.07157), Actionable Interpretability Workshop (non-archival) at the 42nd ICML; [EEG, Image, Text]\n- [BrainFLORA: Uncovering Brain Concept Representation via Multimodal Neural Embeddings](https://arxiv.org/abs/2507.09747), ACM Multimedia 2025; [Image, EEG, fMRI, MEG]\n- [Prompting the Mind: EEG-to-Text Translation with Multimodal LLMs and Semantic Control](https://link.springer.com/chapter/10.1007/978-3-032-07956-5_4), Speech and Computer 2025; [EEG, Text]\n- [NeuroCognitor: Unified EEG-Language Framework for Cognitive Load Analysis via Instruction-Tuned Multi-Task Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=11269754), IEEE Access 2025; [EEG, Text]\n\n### ECG :anatomical_heart: + X \n- [Comparing Recognition Performance and Robustness of Multimodal Deep Learning Models for Multimodal Emotion Recognition](https://ieeexplore.ieee.org/abstract/document/9395500), IEEE TCDS 2021; [EEG, Eye Movement, Peripheral Physiological Signals, ECG]\n- [Transfer Knowledge from Natural Language to Electrocardiography: Can We Detect Cardiovascular Disease Through Language Models?](https://arxiv.org/pdf/2301.09017.pdf), EACL Findings 2023; [ECG, Text]\n- [Converting ECG Signals to Images for Efficient Image-text Retrieval via Encoding](https://arxiv.org/pdf/2304.06286), Machine Learning for Health 2023; [ECG, Image, Text]\n- [An Ensemble Learning Method for Emotion Charting Using Multimodal Physiological Signals](https://www.mdpi.com/1424-8220/22/23/9480), Sensors 2022; [EEG, ECG, GSR]\n- [Automated detection of panic disorder based on multimodal physiological signals using machine learning](https://onlinelibrary.wiley.com/doi/full/10.4218/etrij.2021-0299), ETRI Journal 2022; [ECG, EDA, Respiration, Peripheral Temperature]\n- [ECGBERT: Understanding Hidden Language of ECGs with Self-Supervised Representation Learning](https://arxiv.org/abs/2306.06340), arxiv 2023; [ECG, Text]\n- [ECG Language Processing (ELP): A new technique to analyze ECG signals](https://www.sciencedirect.com/science/article/pii/S0169260721000341), Computer Methods and Programs in Biomedicine; [ECG, Text]\n- [Robust Patient Information Embedding and Retrieval Mechanism for ECG Signals](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=\u0026arnumber=9201451), IEEE Access 2020; [ECG, Text]\n- [Multimodal Physiological Signals and Machine Learning for Stress Detection by Wearable Devices](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=\u0026arnumber=9856558), IEEE MEMEA 2022; [EDA, ECG, PPG]\n- [Multimodal Physiological Signals Fusion for Online Emotion Recognition](https://dl.acm.org/doi/pdf/10.1145/3581783.3612555), ACM Multimedia 2023; [EEG, ECG, GSR]\n- [Graph to Grid: Learning Deep Representations for Multimodal Emotion Recognition](https://dl.acm.org/doi/pdf/10.1145/3581783.3612074), ACM Multimedia 2023; [EEG, Eye Movement, GSR, Respiration, ECG]\n- [Automated detection of panic disorder based on multimodal physiological signals using machine learning](https://onlinelibrary.wiley.com/doi/epdf/10.4218/etrij.2021-0299?src=getftr), ETRI Journal 2023; [ECG, EDA, RESP, PT]\n- [Stress Classification by Multimodal Physiological Signals Using Variational Mode Decomposition and Machine Learning](https://www.semanticscholar.org/reader/f23f2f2a8bd3f80b44042fd5b58c57c76fcdf281), Hindawi Journal of Healthcare Engineering 2021; [ECG, EEG]\n- [Validation and Interpretation of a Multimodal Drowsiness Detection System Using Explainable Machine Learning](https://www.sciencedirect.com/science/article/pii/S0169260723005916), Computer Methods and Programs in Biomedicine 2023; [EEG, ECG, EOG]\n- [A Novel Algorithm for Movement Artifact Removal in ECG Signals Acquired from Wearable Systems Applied to Horses](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4618928/), PLoS One 2015; [ECG, Accelerometer]\n- [Automatic stress detection in car drivers based on non-invasive physiological signals using machine learning techniques](https://link.springer.com/article/10.1007/s00521-023-08428-w), Neural Computing and Applications 2023; [ECG, EMG, GSR, Respiration Rate]\n- [Hierarchical extreme puzzle learning machine-based emotion recognition using multimodal physiological signals](https://www.sciencedirect.com/science/article/pii/S1746809423000575), Biomedical Signal Processing and Control 2023; [EDA, ECG, TEMP, RESP, EMR]\n- [Machine Learning–Enabled Multimodal Fusion of Intra-Atrial and Body Surface Signals in Prediction of Atrial Fibrillation Ablation Outcomes](https://www.ahajournals.org/doi/full/10.1161/CIRCEP.122.010850), Journal of American Heart Association 2022; [ECG, iECG]\n- [ECG and EEG based detection and multilevel classification of stress using machine learning for specified genders: A preliminary study](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10473514/), PLoS One 2023; [EEG, ECG]\n- [Modeling Multivariate Biosignals With Graph Neural Networks and Structured State Space Models](https://proceedings.mlr.press/v209/tang23a.html), CHIL 2023; [EEG, ECG, PSG]\n- [Cognitive Load Prediction from Multimodal Physiological Signals using Multiview Learning.](https://www.semanticscholar.org/paper/Cognitive-Load-Prediction-from-Multimodal-Signals-Liu-Yu/5103ff5e420ff93bd37ff414e9789fda99e46424), IEEE journal of biomedical and health informatics 2023; [EEG, EDA, ECG, EOG, \u0026 Eye Movements]\n- [ECG Semantic Integrator (ESI): A Foundation ECG Model Pretrained with LLM-Enhanced Cardiological Text](https://arxiv.org/abs/2405.19366), arxiv 2024; [ECG, Text]\n- [MEIT: Multi-Modal Electrocardiogram Instruction Tuning on Large Language Models for Report Generation](https://arxiv.org/pdf/2403.04945), arxiv 2024; [ECG, Text]\n- [An Efficient Multimodal Emotion Identification Using FOX Optimized Double Deep Q-Learning](https://dl.acm.org/doi/10.1007/s11277-023-10685-w), Wireless Personal Communications 2023; [GSR, ECG, and EEG]\n- [A Framework for Cognitive Load Recognition Based on Machine Learning and Multimodal Physiological Signals by Wearable Sensors](https://ieeexplore.ieee.org/abstract/document/10348206), IEEE PRML 2023; [ECG, EDA, and PPG]\n- [A Household Multimodal Physiological Signals Monitoring System](https://ieeexplore.ieee.org/document/10082658), IEEE 2023; [ECG, lLEMG, rLEMG, pBCG]\n- [ECG-QA: A Comprehensive Question Answering Dataset Combined With Electrocardiogram](https://proceedings.neurips.cc/paper_files/paper/2023/hash/d0b67349dd16b83b2cf6167fb4e2be50-Abstract-Datasets_and_Benchmarks.html), NeurIPS 2023; [ECG, Text]\n- [Multimodal 12-lead ECG data classification using multi-label DenseNet for heart disease detection](https://ieeexplore.ieee.org/document/9943957), IEEE DSIT 2022; [HRV, ECG, EHR]\n- [Time Synchronization of Multimodal Physiological Signals through Alignment of Common Signal Types and Its Technical Considerations in Digital Health](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9145353/), J Imaging 2022; [ECG]\n- [Single-modal and multi-modal false arrhythmia alarm reduction using attention-based convolutional and recurrent neural networks](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0226990), PLoS One 2024; [PPG, ABP, ECG]\n- [Negative emotion recognition using multimodal physiological signals for advanced driver assistance systems](https://link.springer.com/article/10.1007/s10015-023-00858-y), Artificial Life and Robotics 2023; [ECG, EDA, EEG, and fNIRS]\n- [Position Paper: What Can Large Language Models Tell Us about Time Series Analysis](https://arxiv.org/pdf/2402.02713.pdf), arxiv 2024, [ECG, Text]\n- [Large Language Model-informed ECG Dual Attention Network for Heart Failure Risk Prediction](https://arxiv.org/abs/2403.10581), arxiv 2024; [ECG, Text]\n- [Frozen Language Model Helps ECG Zero-Shot Learning](https://arxiv.org/pdf/2303.12311.pdf), MIDL 2023; [ECG, Text]\n- [Zero-Shot ECG Diagnosis with Large Language Models and Retrieval-Augmented Generation](https://proceedings.mlr.press/v225/yu23b/yu23b.pdf), ML4H 2023; [ECG, Text]\n- [Physiological Fusion Net: Quantifying Individual VR Sickness with Content Stimulus and Physiological Response](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=8802983), IEEE International Conference on Image Processing (ICIP) 2019; [Vision, EEG, ECG, GSR]\n- [Text-to-ECG: 12-Lead Electrocardiogram Synthesis conditioned on Clinical Text Reports](https://arxiv.org/pdf/2303.09395.pdf), ICASSP 2023; [ECG, Text]\n- [Large Language Models for Time Series: A Survey](https://arxiv.org/pdf/2402.01801.pdf), arxiv 2024; [EEG, ECG, Text, Vision]\n- [Integrating multimodal information in machine learning for classifying acute myocardial infarction](https://pubmed.ncbi.nlm.nih.gov/36963114/), Physiological Measurement 2023; [ECG, Text]\n- [Multimodal explainable artificial intelligence identifies patients with non-ischaemic cardiomyopathy at risk of lethal ventricular arrhythmias](https://pubmed.ncbi.nlm.nih.gov/38937555/), Scientific reports 2024; [MRI, ECG]\n- [Multimodal Estimation Of Change Points Of Physiological Arousal During Driving](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=\u0026arnumber=10193718), ICASSP 2023; [ECG, EDA, EMG, HR, BVP]\n- [Hypercomplex Multimodal Emotion Recognition from EEG and Peripheral Physiological Signals](https://arxiv.org/abs/2310.07648), IEEE ICASSP Workshops 2023; [EEG, ECG, GSR]\n- [Deep learning for ECG classification: A comparative study of 1D and 2D representations and multimodal fusion approaches](https://www.sciencedirect.com/science/article/pii/S174680942400199X), Biomedical Signal Processing and Control 2024; [ECG, Image]\n- [Zero-Shot ECG Classification with Multimodal Learning and Test-time Clinical Knowledge Enhancement](https://arxiv.org/pdf/2403.06659), ICML 2024; [ECG ,Text]\n- [SleepFM: Multi-modal Representation Learning for Sleep Across Brain Activity, ECG and Respiratory Signals](https://raw.githubusercontent.com/mlresearch/v235/main/assets/thapa24a/thapa24a.pdf), ICML 2024; [ECG, EEG, EOG, EMG, Respiratory Sensors]\n- [ECG Heartbeat Classification Using Multimodal Fusion](https://ieeexplore.ieee.org/abstract/document/9486862), IEEE Access 2021; [ECG, Image]\n- [Multimodal ChatGPT-4V for Electrocardiogram Interpretation: Promise and Limitations](https://www.jmir.org/2024/1/e54607/), JMIR 2024; [ECG, Image]\n- [Multimodal ECG heartbeat classification method based on a convolutional neural network embedded with FCA](https://www.nature.com/articles/s41598-024-59311-0), Nature Scientific Reports 2024; [ECG, Image]\n- [Large-scale Training of Foundation Models for Wearable Biosignals](https://arxiv.org/pdf/2312.05409), ICLR 2024; [PPG, ECG]\n- [ECG-Chat: A Large ECG-Language Model for Cardiac Disease Diagnosis](https://arxiv.org/pdf/2408.08849), arxiv 2024; [ECG, Text]\n- [CardioGPT: An ECG Interpretation Generation Model](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=\u0026arnumber=10488393), IEEE Access 2024; [ECG, Text]\n- [Harnessing the Power of ChatGPT in Cardiovascular Medicine: Innovations, Challenges, and Future Directions](https://www.preprints.org/manuscript/202408.2015/v1), preprint 2024; [ECG, Text]\n- [The potential for large language models to transform cardiovascular medicine](https://www.thelancet.com/journals/landig/article/PIIS2589-7500(24)00151-1/fulltext), The Lancet Digital Health 2024; [ECG, Text]\n- [Explainable Artificial Intelligence on Biosignals for Clinical Decision Support](https://dl.acm.org/doi/pdf/10.1145/3637528.3671459), KDD 2024; [EEG, ECG]\n- [Recent Trends of Multimodal Affective Computing: A Survey from NLP Perspective](https://arxiv.org/pdf/2409.07388), arxiv 2024; [EEG, ECG, Audio, Text, Image]\n- [Electrocardiogram Report Generation and Question Answering via Retrieval-Augmented Self-Supervised Modeling](https://arxiv.org/pdf/2409.08788), arxiv 2024; [Text, ECG]\n- [Foundation Models for Cardiovascular Disease Detection via BioSignals from Digital Stethoscopes](https://assets-eu.researchsquare.com/files/rs-4732737/v1_covered_5d102066-509a-4c79-a602-32570aadb619.pdf?c=1722816670), Research Square 2024; [PCG, ECG]\n- [From Hospital to Portables: A Universal ECG Foundation Model Built on 10+ Million Diverse Recordings](https://arxiv.org/pdf/2410.04133), arxiv 2024; [Text, ECG]\n- [Electrocardiogram–Language Model for Few-Shot Question Answering with Meta Learning](https://arxiv.org/pdf/2410.14464), arxiv 2024; [Text, ECG]\n- [Promoting cross-modal representations to improve multimodal foundation models for physiological signals](https://arxiv.org/pdf/2410.16424), Medical Foundation Models (AIM-FM) workshop at NeurIPS 2024; [EEG, EMG, EOG, ECG]\n- [Foundation Models in Electrocardiogram: A Review](https://arxiv.org/pdf/2410.19877), arxiv 2024; [ECG, Text]\n- [C-MELT: Contrastive Enhanced Masked Auto-Encoders for ECG-Language Pre-Training](https://arxiv.org/pdf/2410.02131v1), arxiv 2024; [ECG, Text]\n- [Let Your Heart Speak in its Mother Tongue: Multilingual Captioning of Cardiac Signals](https://arxiv.org/abs/2103.11011), arxiv 2021, [ECG, Text]\n- [Learning under Label Noise through Few-Shot Human-in-the-Loop Refinement](https://arxiv.org/pdf/2401.14107), arxiv 2024; [EEG, IMU, EMG, EOG, ECG]\n- [Precision of artificial intelligence in paediatric cardiology multimodal image interpretation](https://www.cambridge.org/core/services/aop-cambridge-core/content/view/B451CE9897CBE67135E50D9DC0CDEA94/S1047951124036035a.pdf/precision_of_artificial_intelligence_in_paediatric_cardiology_multimodal_image_interpretation.pdf), Cambridge University Press 2024; [ECG, Text]\n- [A Survey of LLMs on Biosignal Applications](https://d197for5662m48.cloudfront.net/documents/publicationstatus/232019/preprint_pdf/a5e9124b3b3daff3a6e7766c3e57f047.pdf), arxiv 2024; [ECG, PPG, EEG, IMU, Text]\n- [HeartBERT: A Self-Supervised ECG Embedding Model for Efficient and Effective Medical Signal Analysis](https://arxiv.org/abs/2411.11896), arxiv 2024; [ECG, Text]\n- [De-biased Multimodal Electrocardiogram Analysis](https://arxiv.org/pdf/2411.14795), arxiv 2024; [ECG, Text]\n- [Cross-Modality Cardiac Insight Transfer: A Contrastive Learning Approach to Enrich ECG with CMR Features](https://link.springer.com/chapter/10.1007/978-3-031-72384-1_11), MICCAI 2024; [ECG, CMR]\n- [Large-scale cross-modality pretrained model enhances cardiovascular state estimation and cardiomyopathy detection from electrocardiograms: An AI system development and multi-center validation study](https://arxiv.org/abs/2411.13602), arxiv 2024; [ECG, CMR]\n- [Automated Medical Report Generation for ECG Data: Bridging Medical Text and Signal Processing with Deep Learning](https://arxiv.org/abs/2412.04067), arxiv 2024; [ECG, Text]\n- [ECHOPulse: ECG controlled echocardio-grams video generation](https://arxiv.org/abs/2410.03143), arxiv 2024; [ECG, ECHO, Video]\n- [Application of Contrastive Learning on ECG Data: Evaluating Performance in Japanese and Classification with Around 100 Labels](https://openreview.net/pdf?id=KzryCCOrWs), arxiv 2024; [ECG, Text]\n- [Foundation model of ECG diagnosis: Diagnostics and explanations of any form and rhythm on ECG](https://www.cell.com/cell-reports-medicine/fulltext/S2666-3791(24)00646-3), Cell Reports Medicine 2024; [ECG, Text]\n- [ECG-LM: Understanding Electrocardiogram with Large Language Model](https://spj.science.org/doi/pdf/10.34133/hds.0221), SPJ 2024; [ECG, Text]\n- [MedTsLLM: Leveraging LLMs for Multimodal Medical Time Series Analysis](https://arxiv.org/pdf/2408.07773), Machine Learning for Healthcare 2024; [ECG, Text]\n- [Develop an emotion recognition system using jointly connectivity between electroencephalogram and electrocardiogram signals](https://www.cell.com/heliyon/fulltext/S2405-8440(25)00147-1), Heliyon CellPress 2025; [ECG, EEG]\n- [Teach Multimodal LLMs to Comprehend Electrocardiographic Images](https://arxiv.org/abs/2410.19008), arxiv 2024; [ECG, Text, Image]\n- [DiffuSETS: 12-lead ECG Generation Conditioned on Clinical Text Reports and Patient-Specific Information](https://arxiv.org/pdf/2501.05932), arxiv 2025; [ECG, Text]\n- [Automated Diagnostic Reports from Images of Electrocardiograms at the Point-of-Care](https://pubmed.ncbi.nlm.nih.gov/38405776/), medRxiv 2024; [ECG, Image, Text]\n- [Multi-Modal Contrastive Learning across Cardiac Diagnostics](https://ai.stanford.edu/~bryanhe/publications/multimodal.pdf), preprint 2024; [Video, ECG, Image, Text]\n- [Enhancing Visual Inspection Capability of Multi-Modal Large Language Models on Medical Time Series with Supportive Conformalized and Interpretable Small Specialized Models](https://arxiv.org/pdf/2501.16215), arxiv 2025; [ECG, Image, Text]\n- [Improving Atrial Fibrillation Detection Using a Shared Latent Space for ECG and PPG Signals](https://hdsr.mitpress.mit.edu/pub/vifgdvlv/release/1), Harvard Data Science Review 2025; [ECG, PPG]\n- [High-Accuracy ECG Image Interpretation using Parameter-Efficient LoRA Fine-Tuning with Multimodal LLaMA 3.2](https://arxiv.org/abs/2501.18670z), arxiv 2025; [ECG, Text]\n- [A Multimodal Sleep Foundation Model Developed with 500K Hours of Sleep Recordings for Disease Predictions](https://www.medrxiv.org/content/10.1101/2025.02.04.25321675v1.full.pdf), medRxiv 2025; [EEG, EOG, EMG, ECG, Respiratory]\n- [Interpretable Pre-Trained Transformers for Heart Time-Series Data](https://www.arxiv.org/pdf/2407.20775), arxiv 2024; [ECG, PPG]\n- [CardioGAN: Attentive Generative Adversarial Network with Dual Discriminators for Synthesis of ECG from PPG](https://arxiv.org/pdf/2010.00104), AAAI 2021; [ECG, PPG]\n- [Signal-quality-aware multisensor fusion for atrial fibrillation detection](https://ietresearch.onlinelibrary.wiley.com/doi/pdfdirect/10.1049/htl2.12121), Healthcare Technology Letters 2025; [ECG, PPG]\n- [Finetuning and Quantization of EEG-Based Foundational BioSignal Models on ECG and PPG Data for Blood Pressure Estimation](https://www.arxiv.org/abs/2502.17460), arxiv 2025; [ECG, PPG, EEG]\n- [IHILLM-RAG: a safe and private medical large language model based on intelligent hardware interaction and retrieval-augmented generation (RAG)](https://www.spiedigitallibrary.org/conference-proceedings-of-spie/13542/135423W/IHILLM-RAG--a-safe-and-private-medical-large-language/10.1117/12.3056789.full), SPIE Digital Library 2025; [ECG, PPG, Text]\n- [SuPreME: A Supervised Pre-training Framework for Multimodal ECG Representation Learning](https://arxiv.org/abs/2502.19668), arxiv 2025; [ECG, Text]\n- [TELL ME: Tackle Electrocardiogram with Large Language Model Effectively](https://ieeexplore.ieee.org/abstract/document/10889496), ICASSP 2025; [ECG, Text]\n- [GEM: Empowering MLLM for Grounded ECG Understanding with Time Series and Images](https://arxiv.org/abs/2503.06073v1), arxiv 2025; [ECG, Image, Text]\n- [ECG Representation Learning with Multi-Modal EHR Data](https://openreview.net/pdf/bf8bd08594d1b484133213a81f685c20a726dec4.pdf), TMLR 2023; [ECG, Text, sEHR]\n- [BioSignal Copilot: Leveraging the power of LLMs in drafting reports for biomedical signals](https://www.medrxiv.org/content/10.1101/2023.06.28.23291916v1.full.pdf), medRxiv 2023; [ECG, Text]\n- [Data Foundations for Large Scale Multimodal Clinical Foundation Models](https://www.arxiv.org/abs/2503.07667), arxiv 2025; [EEG, ECG, Text, Image, Video]\n- [Towards Robust Multimodal Physiological Foundation Models: Handling Arbitrary Missing Modalities](https://arxiv.org/abs/2504.19596), arxiv 2025; [EEG, ECG, EMG, EOG]\n- [PainFormer: a Vision Foundation Model for Automatic Pain Assessment](https://arxiv.org/pdf/2505.01571), arxiv 2025; [ECG, Image, EMG, GSR, fNIRS]\n- [Q-HEART: ECG Question Answering via Knowledge-Informed Multimodal LLMs](https://arxiv.org/abs/2505.06296), arxxiv 2025; [ECG, Text]\n- [Fine-Grained ECG-Text Contrastive Learning via Waveform Understanding Enhancement](https://arxiv.org/abs/2505.11939), arxiv 2025; [ECG, Text]\n- [Signal, Image, or Symbolic: Exploring the Best Input Representation for Electrocardiogram-Language Models Through a Unified Framework](https://arxiv.org/abs/2505.18847), arxiv 2025; [ECG, Text, Image]\n- [anyECG-chat: A Generalist ECG-MLLM for Flexible ECG Input and Multi-Task Understanding](https://arxiv.org/abs/2506.00942), arxiv 2025; [ECG, Text]\n- [Heartcare Suite: Multi-dimensional Understanding of ECG with Raw Multi-lead Signal Modeling](https://arxiv.org/abs/2506.05831), arxiv 2025; [ECG, Text]\n- [TimeMaster: Training Time-Series Multimodal LLMs to Reason via Reinforcement Learning](https://arxiv.org/abs/2506.13705), arxiv 2025; [ECG, EMG, Text]\n- [ECGFM: A foundation model for ECG analysis trained on a multi-center million-ECG dataset](https://www.sciencedirect.com/science/article/pii/S1566253525004361), Informatic Fusion 2025; [ECG, Text]\n- [Efficient Personalized Adaptation for Physiological Signal Foundation Model](https://openreview.net/forum?id=55ysNwbOTI), ICML 2025; [EEG, EOG, EMG, ECG]\n- [ECG-Byte: A Tokenizer for End-to-End Generative Electrocardiogram Language Modeling](https://arxiv.org/abs/2412.14373), MLHC 2025; [ECG, Text]\n- [Sensing Cardiac Health Across Scenarios and Devices: A Multi-Modal Foundation Model Pretrained on Heterogeneous Data from 1.7 Million Individuals](https://arxiv.org/abs/2507.01045), arxiv 2025; [ECG, PPG, Text]\n- [From Token to Rhythm: A Multi-Scale Approach for ECG-Language Pretraining](https://arxiv.org/abs/2506.21803), ICML 2025; [ECG, Text]\n- [TAC-ECG: A task-adaptive classification method for electrocardiogram based on cross-modal contrastive learning and low-rank convolutional adapter](https://www.sciencedirect.com/science/article/pii/S0169260725003359), Computer Methods and Programs in Biomedicine 2025; [ECG, Text]\n- [ECGTwin: Personalized ECG Generation Using Controllable Diffusion Model](https://arxiv.org/abs/2508.02720), arxiv 2025; [ECG, Text]\n- [Deep ECG-Report Interaction Framework for Cross-Modal Representation Learning](https://openreview.net/forum?id=0quBGOPP5V), arxiv 2025; [ECG, Text]\n- [DiagECG: An LLM-Driven Framework for Diagnostic Reasoning via Discretized ECG Tokenization](https://arxiv.org/abs/2508.15338), arxiv 2025; [ECG, Text]\n- [MedTVT-R1: A Multimodal LLM Empowering Medical Reasoning and Diagnosis](https://arxiv.org/pdf/2506.18512), arxiv 2025; [ECG, Image, Text]\n- [TolerantECG: A Foundation Model for Imperfect Electrocardiogram](https://arxiv.org/abs/2507.09887), ACM Multimedia 2025; [ECG, Text]\n- [Synthesizing ECG from BCG: a Physiological Semantics Enhanced Multiband Diffusion Generative Approach](https://ieeexplore.ieee.org/abstract/document/11145806), IEEE Journal of Biomedical and Health Informatics 2025; [BCG, ECG]\n- [Speech Foundation Models Generalize to Time Series Tasks from Wearable Sensor Data](https://arxiv.org/pdf/2509.00221), arxiv 2025; [ECG, IMU, PPG, Audio]\n- [TELL ME: Tackle Electrocardiogram with Large Language Model Effectively](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=\u0026arnumber=10889496), ICASSP 2025; [ECG, Text]\n- [OpenTSLM: Time-Series Language Models for Reasoning over Multivariate Medical Text- and Time-Series Data](https://www.researchgate.net/profile/Patrick-Langer-5/publication/395975374_OpenTSLM_Time-Series_Language_Models_for_Reasoning_over_Multivariate_Medical_Text-_and_Time-Series_Data/links/68db94a8ffdca73694b478be/OpenTSLM-Time-Series-Language-Models-for-Reasoning-over-Multivariate-Medical-Text-and-Time-Series-Data.pdf), arxiv 2025; [ECG, PSG, Text]\n- [CLEF: Clinically-Guided Contrastive Learning for Electrocardiogram Foundation Models](https://www.arxiv.org/abs/2512.02180), arxiv 2025; [ECG, Text]\n- [Towards Clinically Faithful ECG Reports via Quantization-Based Tokenization](https://openreview.net/pdf?id=MJCDK4mnAa), NeurIPS 2025 Workshop BrainBodyFM; [ECG, Text]\n- [UniPACT: A Multimodal Framework for Prognostic Question Answering on Raw ECG and Structured EHR](https://arxiv.org/abs/2601.17916v1), ICASSP 2026; [ECG, Text]\n- [ELF: A Family of Encoder-Free ECG-Language Models](https://arxiv.org/abs/2601.18798), MLHC 2026; [ECG, Text]\n- [ECG-Agent: On-Device Tool-Calling Agent for ECG Multi-Turn Dialogue](https://arxiv.org/abs/2601.20323), ICASSP 2026; [ECG, Text]\n- [ECG-R1: Protocol-Guided and Modality-Agnostic MLLM for Reliable ECG Interpretation](https://arxiv.org/pdf/2602.04279), arxiv 2026; [ECG, Text, Image]\n- [ECG-LLM: Foundation Model for ECG-Based Cardiac Reasoning](https://arxiv.org/abs/2607.16323), arxiv 2026; [ECG, Text]\n\n### EDA :sweat_drops: + X\n- [A Multimodal Music Recommendation System with Listeners' Personality and Physiological Signals](https://dl.acm.org/doi/abs/10.1145/3383583.3398623), ACM/IEEE JCDL 2020; [Heart Rate, EDA, IBI, Skin Temperature, BVP]\n- [Stress Detection with Deep Learning Approaches Using Physiological Signals](https://link.springer.com/chapter/10.1007/978-3-030-69963-5_7), IoT Technologies for Healthcare 2020; [EDA, BVP]\n- [Multimodal Physiological Signals and Machine Learning for Stress Detection by Wearable Devices](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=\u0026arnumber=9856558), IEEE MEMEA 2022; [EDA, ECG, PPG]\n- [Automated detection of panic disorder based on multimodal physiological signals using machine learning](https://onlinelibrary.wiley.com/doi/epdf/10.4218/etrij.2021-0299?src=getftr), ETRI Journal 2023; [ECG, EDA, RESP, PT]\n- [Transformer-Based Physiological Feature Learning for Multimodal Analysis of Self-Reported Sentiment](https://dl.acm.org/doi/abs/10.1145/3536221.3556576), ICMI 2022; [EDA, BVP, HR, TEMP]\n- [Hierarchical extreme puzzle learning machine-based emotion recognition using multimodal physiological signals](https://www.sciencedirect.com/science/article/pii/S1746809423000575), Biomedical Signal Processing and Control 2023; [EDA, ECG, TEMP, RESP, EMR]\n- [A Hybrid Deep Learning Emotion Classification System Using Multimodal Data](https://www.mdpi.com/1424-8220/23/23/9333), Sensors 2023; [EDA, IBI, TEMP, Text, Audio]\n- [Exploring the Potential of Multimodal Emotion Recognition for Hearing-Impaired Children Using Physiological Signals and Facial Expressions](https://dl.acm.org/doi/pdf/10.1145/3610661.3616240), ICMI Companion 2023; [ST, EDA, BVP, ACC, HR, Vision]\n- [Performance Exploration of RNN Variants for Recognizing Daily Life Stress Levels by Using Multimodal Physiological Signals](https://dl.acm.org/doi/pdf/10.1145/3577190.3614159), ICMI 2023; [EDA, ST, ACC, HR]\n- [Transformer-based Self-supervised Multimodal Representation Learning for Wearable Emotion Recognition](https://arxiv.org/abs/2303.17611), IEEE Transactions On Affective Computing 2023; [EDA, BVP, TEMP]\n- [Cognitive Load Prediction from Multimodal Physiological Signals using Multiview Learning.](https://www.semanticscholar.org/paper/Cognitive-Load-Prediction-from-Multimodal-Signals-Liu-Yu/5103ff5e420ff93bd37ff414e9789fda99e46424), IEEE journal of biomedical and health informatics 2023; [EEG, EDA, ECG, EOG, \u0026 Eye Movements]\n- [A Framework for Cognitive Load Recognition Based on Machine Learning and Multimodal Physiological Signals by Wearable Sensors](https://ieeexplore.ieee.org/abstract/document/10348206), IEEE PRML 2023; [ECG, EDA, and PPG]\n- [Stress appraisal in the workplace and its associations with productivity and mood: Insights from a multimodal machine learning analysis](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0296468), PLoS One 2024; [EDA, ST, BVP, Accelerometer]\n- [Effects of Physiological Signals in Different Types of Multimodal Sentiment Estimation](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=\u0026arnumber=9726810\u0026tag=1), IEEE AC 2023; [Language, EDA, Audio, Vision]\n- [Negative emotion recognition using multimodal physiological signals for advanced driver assistance systems](https://link.springer.com/article/10.1007/s10015-023-00858-y), Artificial Life and Robotics 2023; [ECG, EDA, EEG, and fNIRS]\n- [An Exploratory Study of Multimodal Physiological Data in Jazz Improvisation Using Basic Machine Learning Techniques](https://arxiv.org/ftp/arxiv/papers/2401/2401.12266.pdf), arxiv 2024; [EDA, EEG]\n- [Multimodal Estimation Of Change Points Of Physiological Arousal During Driving](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=\u0026arnumber=10193718), ICASSP 2023; [ECG, EDA, EMG, HR, BVP]\n\n### Eye Movement :eye: + X \n- [Investigating Sex Differences in Classification of Five Emotions from EEG and Eye Movement Signals](https://drive.google.com/file/d/1pCln0DI3fiIPHHiRiCRcXJWb4LCWskXm/view), IEEE EMBC 2019; [EEG, Eye Movement]\n- [Decoding EEG Brain Activity for Multi-Modal Natural Language Processing](https://www.frontiersin.org/articles/10.3389/fnhum.2021.659410/full?\u0026utm_source=Email_to_authors_\u0026utm_medium=Email\u0026utm_content=T1_11.5e1_author\u0026utm_campaign=Email_publication\u0026field=\u0026journalName=Frontiers_in_Human_Neuroscience\u0026id=659410), Frontiers in Human Neuroscience 2021; [EEG, Eye Movement, Text]\n- [Multi-view Emotion Recognition Using Deep Canonical Correlation Analysis](https://bcmi.sjtu.edu.cn/~blu/papers/2018/Qiu2018Multi-viewEmotion.pdf), ICONIP 2018; [EEG, Eye Movement]\n- [Correlated Attention Networks for Multimodal Emotion Recognition](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=8621129), IEEE BIBM 2018; [EEG, Eye Movement]\n- [Multimodal Emotion Recognition Using Deep Neural Networks](https://bcmi.sjtu.edu.cn/~liuwei/Wei%20Liu's%20HomePage_files/th_paper_2017.pdf), ICONIP 2017; [EEG, Eye Movement]\n- [EmotionMeter: A Multimodal Framework for Recognizing Human Emotions](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=\u0026arnumber=8283814), IEEE Transactions on Cybernetics 2019; [EEG, Eye Movement]\n- [Multimodal Adaptive Emotion Transformer with Flexible Modality Inputs on A Novel Dataset with Continuous Labels](https://www.semanticscholar.org/paper/Multimodal-Adaptive-Emotion-Transformer-with-Inputs-Jiang-Liu/a08b89ea7269be93bbe0d7f5f8493471b4d1c39c), ACM 2023; [EEG, Eye Movement]\n- [Emotion Transformer Fusion: Complementary Representation Properties of EEG and Eye Movements on Recognizing Anger and Surprise](https://bcmi.sjtu.edu.cn/~lubaoliang/papers/2021/2021-14.pdf), IEEE BIBM 2023; [EEG, Eye Movement]\n- [Graph to Grid: Learning Deep Representations for Multimodal Emotion Recognition](https://dl.acm.org/doi/pdf/10.1145/3581783.3612074), ACM Multimedia 2023; [EEG, Eye Movement, GSR, Respiration, ECG]\n- [Cognitive Load Prediction from Multimodal Physiological Signals using Multiview Learning.](https://www.semanticscholar.org/paper/Cognitive-Load-Prediction-from-Multimodal-Signals-Liu-Yu/5103ff5e420ff93bd37ff414e9789fda99e46424), IEEE journal of biomedical and health informatics 2023; [EEG, EDA, ECG, EOG, \u0026 Eye Movements]\n- [Navigating Brain Language Representations: A Comparative Analysis of Neural Language Models and Psychologically Plausible Models](https://arxiv.org/abs/2404.19364), arxiv 2024; [fMRI, Eye Movement]\n- [A Multitask Framework for Emotion Recognition Using EEG and Eye Movement Signals with Adversarial Training and Attention Mechanism](https://ieeexplore.ieee.org/abstract/document/10385505), IEEE BIBM 2023; [EEG, Eye Movement]\n- [Naturalistic Emotion Recognition Using EEG and Eye Movements](https://bcmi.sjtu.edu.cn/home/blu/papers/2023/2023-8.pdf), Springer Nature Singapore 2023; [EEG, Eye Movement]\n- [Multimodal Multi-View Spectral-Spatial-Temporal Masked Autoencoder for Self-Supervised Emotion Recognition](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10447194), ICASSP 2024; [EEG, Eye Movement]\n- [Functional Emotion Transformer for EEG-Assisted Cross-Modal Emotion Recognition](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10446937), ICASSP 2024; [EEG, Eye Movement]\n- [EEG2Video: Towards Decoding Dynamic Visual Perception from EEG Signals](https://openreview.net/forum?id=RfsfRn9OFd), NeurIPS 2024; [EEG, fMRI, Video, Text]\n- [From EEG to Eye Movements: Cross-modal Emotion Recognition Using Constrained Adversarial Network with Dual Attention](https://ieeexplore.ieee.org/abstract/document/10818647), IEEE Transactions on Affective Computing 2024; [EEG, Eye Movement]\n\n\n### EOG :eye: + X \n- [Machine-Learning-Based Detection of Craving for Gaming Using Multimodal Physiological Signals: Validation of Test-Retest Reliability for Practical Use](https://www.mdpi.com/1424-8220/19/16/3475), Sensors 2019; [PPG, GSR, EOG]\n- [A Multimodal Approach to Estimating Vigilance Using EEG and Forehead EOG](https://iopscience.iop.org/article/10.1088/1741-2552/aa5a98/pdf), Journal of Neural Engineering 2017; [EEG, EOG]\n- [A Deep Learning Architecture for Temporal Sleep Stage Classification Using Multivariate and Multimodal Time Series](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=8307462), IEEE Transactions on Neural Systems and Rehabilitation Engineering 2018; [EEG, EOG, EMG]\n- [Validation and Interpretation of a Multimodal Drowsiness Detection System Using Explainable Machine Learning](https://www.sciencedirect.com/science/article/pii/S0169260723005916), Computer Methods and Programs in Biomedicine 2023; [EEG, ECG, EOG]\n- [Cognitive Load Prediction from Multimodal Physiological Signals using Multiview Learning.](https://www.semanticscholar.org/paper/Cognitive-Load-Prediction-from-Multimodal-Signals-Liu-Yu/5103ff5e420ff93bd37ff414e9789fda99e46424), IEEE journal of biomedical and health informatics 2023; [EEG, EDA, ECG, EOG, \u0026 Eye Movements]\n- [Multi-Signal Reconstruction Using Masked Autoencoder From EEG During Polysomnography](https://arxiv.org/abs/2311.07868), IEEE BCI 2023; [EEG, EOG, Chin EMG, Event Makers]\n- [Multimodal Physiological Signal Emotion Recognition Based on Convolutional Recurrent Neural Network](https://iopscience.iop.org/article/10.1088/1757-899X/782/3/032005), IOP 2020; [EOG, EMG, GSR, RSP, BVP, TMP, EEG]\n- [Comparative analysis of physiological signals and electroencephalogram (EEG) for multimodal emotion recognition using generative models](https://ieeexplore.ieee.org/abstract/document/7010181), IEEE STSIVA 2014; [EEG, EOG, EMG, GSR, HR]\n- [SleepFM: Multi-modal Representation Learning for Sleep Across Brain Activity, ECG and Respiratory Signals](https://raw.githubusercontent.com/mlresearch/v235/main/assets/thapa24a/thapa24a.pdf), ICML 2024; [ECG, EEG, EOG, EMG, Respiratory Sensors]\n- [A generative foundation model for five-class sleep staging with arbitrary sensor input](https://www.arxiv.org/abs/2408.15253), arxiv 2024; [EEG, EOG, EMG]\n- [Promoting cross-modal representations to improve multimodal foundation models for physiological signals](https://arxiv.org/pdf/2410.16424), Medical Foundation Models (AIM-FM) workshop at NeurIPS 2024; [EEG, EMG, EOG, ECG]\n- [Learning under Label Noise through Few-Shot Human-in-the-Loop Refinement](https://arxiv.org/pdf/2401.14107), arxiv 2024; [EEG, IMU, EMG, EOG, ECG]\n- [PedSleepMAE: Generative Model for Multimodal Pediatric Sleep Signals](https://arxiv.org/pdf/2411.00718), IEEE International Conference on Biomedical and Health Informatics 2024 2024; [EEG, EMG, EOG]\n- [A Multimodal Sleep Foundation Model Developed with 500K Hours of Sleep Recordings for Disease Predictions](https://www.medrxiv.org/content/10.1101/2025.02.04.25321675v1.full.pdf), medRxiv 2025; [EEG, EOG, EMG, ECG, Respiratory]\n- [Towards Robust Multimodal Physiological Foundation Models: Handling Arbitrary Missing Modalities](https://arxiv.org/abs/2504.19596), arxiv 2025; [EEG, ECG, EMG, EOG]\n- [Robust Sleep Staging over Incomplete Multimodal Physiological Signals via Contrastive Imagination](https://papers.nips.cc/paper_files/paper/2024/file/cb0f9020c00fc52a9f6c9dbfacc6ac58-Paper-Conference.pdf), NeurIPS 2024; [EEG, EMG, EOG]\n- [Efficient Personalized Adaptation for Physiological Signal Foundation Model](https://openreview.net/forum?id=55ysNwbOTI), ICML 2025; [EEG, EOG, EMG, ECG]\n\n### EMG :muscle: + X\n- [A Deep Learning Architecture for Temporal Sleep Stage Classification Using Multivariate and Multimodal Time Series](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=8307462), IEEE Transactions on Neural Systems and Rehabilitation Engineering 2018; [EEG, EOG, EMG]\n- [Automatic stress detection in car drivers based on non-invasive physiological signals using machine learning techniques](https://link.springer.com/article/10.1007/s00521-023-08428-w), Neural Computing and Applications 2023; [ECG, EMG, GSR, Respiration Rate]\n- [Multi-Signal Reconstruction Using Masked Autoencoder From EEG During Polysomnography](https://arxiv.org/abs/2311.07868), IEEE BCI 2023; [EEG, EOG, Chin EMG, Event Makers]\n- [Multimodal Physiological Signal Emotion Recognition Based on Convolutional Recurrent Neural Network](https://iopscience.iop.org/article/10.1088/1757-899X/782/3/032005), IOP 2020; [EOG, EMG, GSR, RSP, BVP, TMP, EEG]\n- [Comparative analysis of physiological signals and electroencephalogram (EEG) for multimodal emotion recognition using generative models](https://ieeexplore.ieee.org/abstract/document/7010181), IEEE STSIVA 2014; [EEG, EOG, EMG, GSR, HR]\n- [Multimodal Estimation Of Change Points Of Physiological Arousal During Driving](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=\u0026arnumber=10193718), ICASSP 2023; [ECG, EDA, EMG, HR, BVP]\n- [SleepFM: Multi-modal Representation Learning for Sleep Across Brain Activity, ECG and Respiratory Signals](https://raw.githubusercontent.com/mlresearch/v235/main/assets/thapa24a/thapa24a.pdf), ICML 2024; [ECG, EEG, EOG, EMG, Respiratory Sensors]\n- [A generative foundation model for five-class sleep staging with arbitrary sensor input](https://www.arxiv.org/abs/2408.15253), arxiv 2024; [EEG, EOG, EMG]\n- [Embracing Large Language and Multimodal Models for Prosthetic Technologies](https://arxiv.org/pdf/2403.04974), arxiv 2024; [EMG, Image, Video, Text, Audio]\n- [EMG-to-Speech: Direct Generation of Speech From Facial Electromyographic Signals](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=\u0026arnumber=8114359), IEEE/ACM Transactions on Audio, Speech, and Language Processing 2024; [EMG, Speech]\n- [Promoting cross-modal representations to improve multimodal foundation models for physiological signals](https://arxiv.org/pdf/2410.16424), Medical Foundation Models (AIM-FM) workshop at NeurIPS 2024; [EEG, EMG, EOG, ECG]\n- [Learning under Label Noise through Few-Shot Human-in-the-Loop Refinement](https://arxiv.org/pdf/2401.14107), arxiv 2024; [EEG, IMU, EMG, EOG, ECG]\n- [PedSleepMAE: Generative Model for Multimodal Pediatric Sleep Signals](https://arxiv.org/pdf/2411.00718), IEEE International Conference on Biomedical and Health Informatics 2024 2024; [EEG, EMG, EOG]\n- [A Multimodal Sleep Foundation Model Developed with 500K Hours of Sleep Recordings for Disease Predictions](https://www.medrxiv.org/content/10.1101/2025.02.04.25321675v1.full.pdf), medRxiv 2025; [EEG, EOG, EMG, ECG, Respiratory]\n- [Towards Robust Multimodal Physiological Foundation Models: Handling Arbitrary Missing Modalities](https://arxiv.org/abs/2504.19596), arxiv 2025; [EEG, ECG, EMG, EOG]\n- [Robust Sleep Staging over Incomplete Multimodal Physiological Signals via Contrastive Imagination](https://papers.nips.cc/paper_files/paper/2024/file/cb0f9020c00fc52a9f6c9dbfacc6ac58-Paper-Conference.pdf), NeurIPS 2024; [EEG, EMG, EOG]\n- [PainFormer: a Vision Foundation Model for Automatic Pain Assessment](https://arxiv.org/pdf/2505.01571), arxiv 2025; [ECG, Image, EMG, GSR, fNIRS]\n- [TimeMaster: Training Time-Series Multimodal LLMs to Reason via Reinforcement Learning](https://arxiv.org/abs/2506.13705), arxiv 2025; [ECG, EMG, Text]\n- [Efficient Personalized Adaptation for Physiological Signal Foundation Model](https://openreview.net/forum?id=55ysNwbOTI), ICML 2025; [EEG, EOG, EMG, ECG]\n\n### Other :placard: + X\n- [Multimodal Representation Learning of Cardiovascular Magnetic Resonance Imaging](https://arxiv.org/pdf/2304.07675.pdf), ICML Workshop on Machine Learning for Multimodal Healthcare Data 2023; [CMR, Text]\n- [Biometric Recognition Using Multimodal Physiological Signals](https://ieeexplore.ieee.org/document/8740847), IEEE 2019; [Heart Rate, Breathing Rate, Palm Electrodermal Activity, Perinasal Perspitation]\n- [Video-based multimodal spontaneous emotion recognition using facial expressions and physiological signals](https://openaccess.thecvf.com/content/CVPR2022W/ABAW/papers/Ouzar_Video-Based_Multimodal_Spontaneous_Emotion_Recognition_Using_Facial_Expressions_and_Physiological_CVPRW_2022_paper.pdf), CVPR Workshop 2022; [Image, iPPG, Heart Rate]\n- [Emotion Recognition from Multimodal Physiological Signals for Emotion Aware Healthcare Systems](https://link.springer.com/article/10.1007/s40846-019-00505-7), Journal of Medical and Biological Engineering 2020; [Respiratory Belt, Photoplethysmography, Fingertip Temperature]\n- [Every word counts: A multilingual analysis of individual human alignment with model attention](https://arxiv.org/abs/2210.04963), AACl 2022; [Eye-Tracking, Text]\n- [Early Life Stress Detection Using Physiological Signals and Machine Learning Pipelines](https://www.mdpi.com/2079-7737/12/1/91), Biology 2023; [Heart Rate, GSR]\n- [Comparative Analysis of Emotion Classification Based on Facial Expression and Physiological Signals Using Deep Learning](https://www.mdpi.com/2076-3417/12/3/1286), Applied Sciences 2022; [Facial Expressions, HRV]\n- [Towards Autonomous Physiological Signal Extraction From Thermal Videos Using Deep Learning](https://dl.acm.org/doi/10.1145/3577190.3614123), ICMI 2023; [HR, Respiration Rate, Temperature]\n- [MAD: Multi-Alignment MEG-to-Text Decoding](https://arxiv.org/abs/2406.01512), arxiv 2024; [MEG, Text]\n- [BTS: Bridging Text and Sound Modalities for Metadata-Aided Respiratory Sound Classification](https://arxiv.org/abs/2406.06786), InterSpeech 2024; [Respiratory Sound, Text]\n- [Brant: Foundation Model for Intracranial Neural Signal](https://proceedings.neurips.cc/paper_files/paper/2023/hash/535915d26859036410b0533804cee788-Abstract-Conference.html), NeurIPS 2023; [iEEG]\n- [BrainBERT: Self-supervised representation learning for intracranial recordings](https://arxiv.org/abs/2302.14367), ICLR 2023; [iEEG]\n- [Evaluating Large Language Models on Time Series Feature Understanding: A Comprehensive Taxonomy and Benchmark](https://arxiv.org/pdf/2404.16563), arxiv 2024; [Time Series Agnostic]\n- [TOTEM: TOkenized T ime Series EMbeddings for General Time Series Analysis](https://arxiv.org/pdf/2402.16412), arxiv 2024; [Time Series Agnostic]\n- [SIM-CNN: Self-Supervised Individualized Multimodal Learning for Stress Prediction on Nurses Using Biosignals](https://www.medrxiv.org/content/medrxiv/early/2023/08/28/2023.08.25.23294640.full.pdf), medRxiv 2023; [EDA, HR, ST, Interbeat Interval, BVP, Accelerometer]\n- [Interpretation of Intracardiac Electrograms Through Textual Representations](https://arxiv.org/abs/2402.01115), CHIL 2024; [EGM, Text]\n- [Multimodal Risk Prediction with Physiological Signals, Medical Images and Clinical Notes](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10246140/), medRxiv 2023; [Vision, Text, EHR]\n- [Do Self-Supervised Speech and Language Models Extract Similar Representations as Human Brain?](https://ieeexplore.ieee.org/abstract/document/10446334), ICASSP 2024; [ECoG, Speech, Text]\n- [Emotion Recognition From Multimodal Physiological Signals via Discriminative Correlation Fusion With a Temporal Alignment Mechanism](https://pubmed.ncbi.nlm.nih.gov/37862275/), IEEE Transactions on Cybernetics 2023; [CNS, ANS]\n- [Health-LLM: Large Language Models for Health Prediction via Wearable Sensor Data](https://arxiv.org/abs/2401.06866), CHIL 2024; [Time Series Agnostic]\n- [BIOT: Biosignal Transformer for Cross-data Learning in the Wild](https://proceedings.neurips.cc/paper_files/paper/2023/hash/f6b30f3e2dd9cb53bbf2024402d02295-Abstract-Conference.html), Neurips 2023; [Time Series Agnostic]\n- [PhysioMTL: Personalizing Physiological Patterns using Optimal Transport Multi-Task Regression](https://proceedings.mlr.press/v174/zhu22a.html), CHIL 2022; [Time Series Agnostic]\n- [Transformers in biosignal analysis: A review](https://www.sciencedirect.com/science/article/pii/S1566253524004755), Elsevier Information Fusion 2024; [Time Series Agnostic]\n- [Towards a Personal Health Large Language Model](https://arxiv.org/pdf/2406.06474), arxiv 2024; [Time Series Agnostic]\n- [Event-Based Contrastive Learning for Medical Time Series](https://arxiv.org/pdf/2312.10308), MLHC 2024; [Time Series Agnostic]\n- [PaPaGei: Open Foundation Models for Optical Physiological Signals](https://arxiv.org/abs/2410.20542), arxiv 2024; [PPG]\n- [Multi-Modal Forecaster: Jointly Predicting Time Series and Textual Data](https://arxiv.org/abs/2411.06735v1), arxiv 2024; [Time Series Agnostic]\n- [COCOA: Cross Modality Contrastive Learning for Sensor Data](https://dl.acm.org/doi/abs/10.1145/3550316), Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 2022; [Time Series Agnostic]\n- [Language Models Still Struggle to Zero-shot Reason about Time Series](https://arxiv.org/pdf/2404.11757), arxiv 2024; [Time Series Agnostic]\n- [TimeSeriesExam: A time series understanding exam](https://arxiv.org/pdf/2410.14752), NeurIPS 2024 Time Series in the Age of Large Models Workshop; [Time Series Agnostic]\n- [LLM-ABBA: Understand time series via symbolic approximation](https://arxiv.org/pdf/2411.18506), arxiv 2024; [Time Series Agnostic]\n- [ChatTS: Aligning Time Series with LLMs via Synthetic Data for Enhanced Understanding and Reasoning](https://arxiv.org/pdf/2412.03104), arxiv 2024; [Time Series Agnostic]\n- [LETS-C: Leveraging Text Embedding for Time Series Classification](https://openreview.net/pdf?id=sPQ46iZNk6), arxiv 2024; [Time Series Agnostic]\n- [Wearable Accelerometer Foundation Models for Health via Knowledge Distillation](https://arxiv.org/abs/2412.11276), arxiv 2024; [PPG, Accelerometer]\n- [A Multimodal Sleep Foundation Model Developed with 500K Hours of Sleep Recordings for Disease Predictions](https://www.medrxiv.org/content/10.1101/2025.02.04.25321675v1.full.pdf), medRxiv 2025; [EEG, EOG, EMG, ECG, Respiratory]\n- [ADAPTive Input Training for Many-to-One Pre-Training on Time-Series Classification](https://d197for5662m48.cloudfront.net/documents/publicationstatus/244221/preprint_pdf/f0dc837fa3aa0bf62f43d0365317880e.pdf), arxiv 2025; [Time Series Agnostic]\n- [AAD-LLM: Neural Attention-Driven Auditory Scene Understanding](https://arxiv.org/abs/2502.16794), arxiv 2025; [iEEG, Text, Audio]\n- [By My Eyes: Grounding Multimodal Large Language Models with Sensor Data via Visual Prompting](https://aclanthology.org/2024.emnlp-main.133/), EMNLP 2025; [Time Series Agnostic]\n- [SACM: SEEG-Audio Contrastive Matching for Chinese Speech Decoding](https://arxiv.org/pdf/2505.19652), arxiv 2025; [SEEG, Audio]\n- [SensorLM: Learning the Language of Wearable Sensors](https://www.arxiv.org/abs/2506.09108), arxiv 2025; [PPG, ACC, EDA, TEMP, ALT]\n- [The Anatomy of a Personal Health Agent](https://arxiv.org/abs/2508.20148), arxiv 2025; [Text, Time Series Agnostic]\n- [Instruction-based Time Series Editing](https://arxiv.org/abs/2508.01504), arxiv 2025; [Text, Time Series Agnostic]\n- [Towards Editing Time Series](https://openreview.net/pdf?id=qu5NTwZtxA), NeurIPS 2024; [Text, Time Series Agnostic]\n- [Time Series, Vision, and Language: Exploring the Limits of Alignment in Contrastive Representation Spaces](https://arxiv.org/pdf/2602.19367), arxiv 2026; [Vision, Text, Time Series Agnostic]\n- [Adaptive Time Series Reasoning via Segment Selection](https://arxiv.org/pdf/2602.18645), arxiv 2026; [Text, Time Series Agnostic]\n\n## Datasets\n- [SEED](https://bcmi.sjtu.edu.cn/home/seed/seed.html); [EEG, Eye Movement, Video]\n- [SEED-IV](https://bcmi.sjtu.edu.cn/home/seed/seed-iv.html); [EEG, Eye Movement, Video]\n- [SEED-VIG](https://bcmi.sjtu.edu.cn/home/seed/seed-vig.html); [EEG, EOG]\n- [SEED-V](https://bcmi.sjtu.edu.cn/home/seed/seed-v.html); [EEG, Eye Movement, Video]\n- [SEED-GER](https://bcmi.sjtu.edu.cn/home/seed/seed-GER.html); [EEG, Eye Movement, Video]\n- [SEED-FRA](https://bcmi.sjtu.edu.cn/home/seed/seed-FRA.html); [EEG, Eye Movement, Video]\n- [ZuCo 2.0](https://osf.io/2urht/); [EEG, Text, Eye Movement]\n- [DEAP](https://www.eecs.qmul.ac.uk/mmv/datasets/deap/); [EEG, Peripheral Physiological Signals, Video]\n- [K-EmoCon](https://zenodo.org/record/3762962); [EEG, Peripheral Physiological Signals, Video, Audio]\n- [AMIGOS](http://www.eecs.qmul.ac.uk/mmv/datasets/amigos/index.html); [EEG, ECG, GSR, Video]\n- [Multimodal Adaptive Emotion Transformer with Flexible Modality Inputs on A Novel Dataset with Continuous Labels](https://www.semanticscholar.org/paper/Multimodal-Adaptive-Emotion-Transformer-with-Inputs-Jiang-Liu/a08b89ea7269be93bbe0d7f5f8493471b4d1c39c); [EEG, Eye Movement]\n- [Dataset of Speech Production in intracranial Electroencephalography](https://www.nature.com/articles/s41597-022-01542-9#code-availability); [Intracranial EEG, Text]\n- [Temple University Hospital EEG Seizure Corpus](https://isip.piconepress.com/projects/tuh_eeg/html/downloads.shtml); [EEG, Artifacts]\n- [ECG \u0026 EEG Stress Features](https://www.kaggle.com/datasets/apithm/ecg-and-eeg-stress-features/); [EEG, ECG]\n- [A multi-modal open dataset for mental-disorder analysis](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9018722/); [EEG, Text]\n- [Sleep-EDF](https://www.physionet.org/content/sleep-edfx/1.0.0/); [EEG, EOG, Chin EMG, Event Makers]\n- [Neurosity EEG Dataset](https://github.com/JeremyNixon/neurosity/tree/main); [EEG]\n- [ECG-QA](https://github.com/Jwoo5/ecg-qa); [ECG, Text]\n- [A Large and Rich EEG Dataset for Modeling Human Visual Object Recognition](https://www.sciencedirect.com/science/article/pii/S1053811922008758); [EEG, Image]\n- [MIMIC-IV-ECG: Diagnostic Electrocardiogram Matched Subset](https://physionet.org/content/mimic-iv-ecg/1.0/); [ECG, EHR, Text]\n- [EEG dataset and OpenBMI toolbox for three BCI paradigms: an investigation into BCI illiteracy](https://academic.oup.com/gigascience/article/8/5/giz002/5304369?login=true); [EEG, EMG]\n- [EIT-1M: One Million EEG-Image-Text Pairs for Human Visual-textual Recognition and More](https://arxiv.org/abs/2407.01884); [EEG, Image, Text]\n- [Alljoined1 - A dataset for EEG-to-Image decoding](https://arxiv.org/pdf/2404.05553); [EEG, Image]\n- [MindBigData](https://mindbigdata.com/opendb/visualmnist.html); [EEG, Image]\n- [Human EEG recordings for 1,854 concepts presented in rapid serial visual presentation streams](https://pubmed.ncbi.nlm.nih.gov/35013331/); [EEG, Image]\n- [EEG-ImageNet: An Electroencephalogram Dataset and Benchmarks with Image Visual Stimuli of Multi-Granularity Labels](https://arxiv.org/pdf/2406.07151v1); [EEG, Image]\n- [The Dutch EEG Speech Register Corpus](https://mirjamernestus.nl/Ernestus/DESRC/Bentum_tenBosch_vandenBosch_Ernestus_DESRC.pdf); [EEG, Audio]\n- [EAV: EEG-Audio-Video Dataset for Emotion Recognition in Conversational Contexts](https://www.nature.com/articles/s41597-024-03838-4); [EEG, Audio, Video]\n- [IMPRESSION](https://ieeexplore.ieee.org/abstract/document/9597421); [ECG, BVP, GSR, Video, Audio, Eye Gaze]\n- [EEVR](https://openreview.net/forum?id=qgzdGyQcDt\u0026noteId=eeMsdS4M2B); [PPG, EDA, Video]\n- [KMED dataset](https://link.springer.com/article/10.1007/s00521-024-10925-5); [EEG, Image, Video]\n- [ECG-Chat Pretrain Dataset](https://www.dropbox.com/scl/fo/ccq5dxmdgg4shf02yjn8c/ANOQ1Hzj4KwHqa1b9r80uzc?rlkey=teysp3v6hg6o9uko2i4zbbjpn\u0026e=1\u0026st=exu3i9oo\u0026dl=0); [ECG, Text]\n- [ECG-Chat Instruction Tuning Dataset](https://github.com/YubaoZhao/ECG-Chat/tree/master/llava/playground/data); [ECG, Text]\n- [PULSE-ECG ECGInstruct Dataset](https://huggingface.co/datasets/PULSE-ECG/ECGInstruct); [ECG, Image, Text]\n- [PULSE-ECG ECGBench Dataset](https://huggingface.co/datasets/PULSE-ECG/ECGBench); [ECG, Image, Text]\n- [BIDMC PPG and Respiration Dataset](https://physionet.org/content/bidmc/1.0.0/); [ECG, PPG]\n- [PPG-DaLiA](https://archive.ics.uci.edu/dataset/495/ppg+dalia); [ECG, PPG]\n- [WESAD (Wearable Stress and Affect Detection)](https://archive.ics.uci.edu/dataset/465/wesad+wearable+stress+and+affect+detection); [ECG, EDA, EMG]\n- [ECG-Expert-QA: A Benchmark for Evaluating Medical Large Language Models in Heart Disease Diagnosis](https://arxiv.org/pdf/2502.17475); [ECG, Text]\n- [ECG-Grounding](https://huggingface.co/datasets/LANSG/ECG-Grounding); [ECG, Image, Text]\n\n\n## Laboratories\n- [Neural Interfacing Lab @ Maastricht University](https://neuralinterfacinglab.github.io/)\n- [BCMI Lab @ Shanghai Jiao Tong University](https://bcmi.sjtu.edu.cn/)\n- [Centre for Language Technology @ University of Copenhagen](https://cst.ku.dk/english/)\n- [Safe AI Lab @ Carnegie Mellon University](https://safeai-lab.github.io/)\n- [NeuroMechatronics Lab @ Carnegie Mellon University](https://www.meche.engineering.cmu.edu/faculty/neuromechatronics-lab.html)\n- [Interactive Computing Lab @ KAIST](https://ic.kaist.ac.kr/)\n- [Laboratory for the Neural Mechanisms of Attention @ UC Davis](https://mangunlab.ucdavis.edu/)\n- [HUman Bio-behavioral Signals Lab @ Texas A\u0026M University](https://hubbs.engr.tamu.edu/)\n- [Computational Intelligence \u0026 Neural Engineering Lab @ Hanyang University](http://cone.hanyang.ac.kr/index_e.html)\n- [Bioelectronics Laboratory @ Incheon National University](https://sites.google.com/view/bioelectronics-lab)\n- [Healthy ML @ MIT](https://healthyml.org/people/)\n- [Auton Lab @ Carnegie Mellon University](https://autonlab.org/)\n- [Biomedical Functional Imaging and Neuroengineering Laboratory @ Carnegie Mellon University](https://www.cmu.edu/bme/helab/)\n- [Laboratoire des Systèmes Perceptifs @ École Normale Supérieure](https://lsp.dec.ens.fr/fr)\n- [Brain and AI Team @ Meta](https://ai.meta.com/blog/studying-the-brain-to-build-ai-that-processes-language-as-people-do/)\n- [Computational Arrhythmia Research Laboratory @ Stanford University](http://web.stanford.edu/group/narayanlab/cgi-bin/wordpress/)\n- [Wu Tsai Institute @ Yale University](https://wti.yale.edu/)\n- [Shah Lab @ Stanford University](https://shahlab.stanford.edu/)\n- [Edward Yoonjae Choi's Lab @ KAIST](https://mp2893.com/index.html)\n- [Scalable Health LAbs @ Rice University](http://sh.rice.edu/)\n- [Pattern Recognition \u0026 Machine Learning Lab @ Korea University](http://pr.korea.ac.kr/)\n- [Signal Analysis and Interpretation Lab @ USC](https://sail.usc.edu/)\n- [DataLearning Group @ Imperial College London](https://www.imperial.ac.uk/data-science/research/research-themes/datalearning/)\n- [Tison Lab @ UCSF](https://tison.ucsf.edu/people-0)\n- [Rajpurkar Lab @ Harvard University](https://www.rajpurkarlab.hms.harvard.edu/)\n- [Wang Lab @ University of Toronto](https://wanglab.ai/)\n- [MAILAB @ Korea University](http://mailab.korea.ac.kr/index.html)\n- [Health AI @ Google](https://ai.google/discover/healthai/)\n- [Health AI @ Apple](https://machinelearning.apple.com/research?domain=Health)\n- [NIAI Lab @ Hanyang University](http://yoonlab.hanyang.ac.kr/people/)\n- [Mobile Systems Research Lab @ University of Cambridge](https://mobile-systems.cl.cam.ac.uk/index.html)\n- [Aaqib Saeed's Lab @ Eindhoven University of Technology](https://aqibsaeed.github.io/)\n- [Multisensory Intelligence Research Group @ MIT](https://www.media.mit.edu/groups/multisensory-intelligence/overview/)\n- [Behavioral Data Science Lab @ University of Washington](https://behavioral-data.github.io/)\n- [AI4Health @ University of Oldenburg](https://uol.de/en/ai4health)\n- [David Ouyang's Lab @ Cedars-Sinai Medical Center](https://douyang.github.io/)\n- [Artificial Intelligence in Medicine (AIMed) Lab @ Ohio State University](https://www.pingzhang.net/lab.html)\n- [Digital Health Lab @ Peking University](https://hsd1503.github.io/)\n- [The Alphanumerics Lab @ Georgia Tech and Emory University](https://sameni.org/)\n- [Centre for Brain-Computing Research @ Nanyang Technological University](https://ntu-cbcr.org/)\n- [SIPLAB @ ETH Zurich](https://siplab.org/)\n- [Health Intelligence Lab @ UCLA](https://web.cs.ucla.edu/~yuzhe/#Lab)\n\n## Citation\nIf you found this repository helpful in your research, please cite the following:\n\n```\n@software{han_2025_15151285,\n  author       = {Han, William},\n  title        = {A corpus of resources for Multimodal Learning with\n                   Physiological Signals\n                  },\n  month        = apr,\n  year         = 2025,\n  publisher    = {Zenodo},\n  version      = {second},\n  doi          = {10.5281/zenodo.15151285},\n  url          = {https://doi.org/10.5281/zenodo.15151285},\n  swhid        = {swh:1:dir:d44f23c5c3ed58e8f53d29c22dab067c30f0769b\n                   ;origin=https://doi.org/10.5281/zenodo.10903466;vi\n                   sit=swh:1:snp:571aa8cdb6e22b70e7833f00c447e7841903\n                   938b;anchor=swh:1:rel:9fc43ffed2239a01f7dc6a4a96c3\n                   ccac93e3dea1;path=willxxy-awesome-mmps-68d74c0\n                  },\n}\n```\n","projects_url":"https://awesome.ecosyste.ms/api/v1/lists/willxxy%2Fawesome-mmps/projects"}