awesome-mmps
Corpus of resources for multimodal machine learning with physiological signals (mmps).
https://github.com/willxxy/awesome-mmps
Last synced: about 8 hours ago
JSON representation
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Datasets
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Other :placard: + X
- SEED
- SEED-IV
- SEED-VIG
- SEED-V
- SEED-GER
- SEED-FRA
- ZuCo 2.0
- DEAP
- AMIGOS
- Temple University Hospital EEG Seizure Corpus
- A multi-modal open dataset for mental-disorder analysis
- Sleep-EDF
- Neurosity EEG Dataset
- A Large and Rich EEG Dataset for Modeling Human Visual Object Recognition
- Dataset of Speech Production in intracranial Electroencephalography
- Dataset of Speech Production in intracranial Electroencephalography
- MIMIC-IV-ECG: Diagnostic Electrocardiogram Matched Subset
- EEG dataset and OpenBMI toolbox for three BCI paradigms: an investigation into BCI illiteracy
- Dataset of Speech Production in intracranial Electroencephalography
- Dataset of Speech Production in intracranial Electroencephalography
- EIT-1M: One Million EEG-Image-Text Pairs for Human Visual-textual Recognition and More
- Alljoined1 - A dataset for EEG-to-Image decoding
- MindBigData
- Dataset of Speech Production in intracranial Electroencephalography
- Human EEG recordings for 1,854 concepts presented in rapid serial visual presentation streams
- Dataset of Speech Production in intracranial Electroencephalography
- ECG-QA
- Dataset of Speech Production in intracranial Electroencephalography
- EEG-ImageNet: An Electroencephalogram Dataset and Benchmarks with Image Visual Stimuli of Multi-Granularity Labels
- Dataset of Speech Production in intracranial Electroencephalography
- Dataset of Speech Production in intracranial Electroencephalography
- Dataset of Speech Production in intracranial Electroencephalography
- Dataset of Speech Production in intracranial Electroencephalography
- Dataset of Speech Production in intracranial Electroencephalography
- Dataset of Speech Production in intracranial Electroencephalography
- Dataset of Speech Production in intracranial Electroencephalography
- Dataset of Speech Production in intracranial Electroencephalography
- The Dutch EEG Speech Register Corpus
- K-EmoCon
- AMIGOS
- Dataset of Speech Production in intracranial Electroencephalography
- EAV: EEG-Audio-Video Dataset for Emotion Recognition in Conversational Contexts
- IMPRESSION
- EEVR
- ECG-Chat Pretrain Dataset
- ECG-Chat Instruction Tuning Dataset
- PULSE-ECG ECGInstruct Dataset
- PULSE-ECG ECGBench Dataset
- BIDMC PPG and Respiration Dataset
- PPG-DaLiA
- WESAD (Wearable Stress and Affect Detection)
- ECG-Expert-QA: A Benchmark for Evaluating Medical Large Language Models in Heart Disease Diagnosis
- ECG-Grounding
- Dataset of Speech Production in intracranial Electroencephalography
- EAV: EEG-Audio-Video Dataset for Emotion Recognition in Conversational Contexts
- Dataset of Speech Production in intracranial Electroencephalography
- EAV: EEG-Audio-Video Dataset for Emotion Recognition in Conversational Contexts
- Dataset of Speech Production in intracranial Electroencephalography
- EAV: EEG-Audio-Video Dataset for Emotion Recognition in Conversational Contexts
- Dataset of Speech Production in intracranial Electroencephalography
- EAV: EEG-Audio-Video Dataset for Emotion Recognition in Conversational Contexts
- Dataset of Speech Production in intracranial Electroencephalography
- EAV: EEG-Audio-Video Dataset for Emotion Recognition in Conversational Contexts
- Dataset of Speech Production in intracranial Electroencephalography
- EAV: EEG-Audio-Video Dataset for Emotion Recognition in Conversational Contexts
- Dataset of Speech Production in intracranial Electroencephalography
- EAV: EEG-Audio-Video Dataset for Emotion Recognition in Conversational Contexts
- Dataset of Speech Production in intracranial Electroencephalography
- EAV: EEG-Audio-Video Dataset for Emotion Recognition in Conversational Contexts
- Dataset of Speech Production in intracranial Electroencephalography
- EAV: EEG-Audio-Video Dataset for Emotion Recognition in Conversational Contexts
- Dataset of Speech Production in intracranial Electroencephalography
- EAV: EEG-Audio-Video Dataset for Emotion Recognition in Conversational Contexts
- Dataset of Speech Production in intracranial Electroencephalography
- A multi-modal open dataset for mental-disorder analysis
- EAV: EEG-Audio-Video Dataset for Emotion Recognition in Conversational Contexts
- EAV: EEG-Audio-Video Dataset for Emotion Recognition in Conversational Contexts
- EAV: EEG-Audio-Video Dataset for Emotion Recognition in Conversational Contexts
- EAV: EEG-Audio-Video Dataset for Emotion Recognition in Conversational Contexts
- EAV: EEG-Audio-Video Dataset for Emotion Recognition in Conversational Contexts
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- K-EmoCon
- Dataset of Speech Production in intracranial Electroencephalography
- Dataset of Speech Production in intracranial Electroencephalography
- Dataset of Speech Production in intracranial Electroencephalography
- Dataset of Speech Production in intracranial Electroencephalography
- Dataset of Speech Production in intracranial Electroencephalography
- Dataset of Speech Production in intracranial Electroencephalography
- ECG & EEG Stress Features
- Dataset of Speech Production in intracranial Electroencephalography
- Dataset of Speech Production in intracranial Electroencephalography
- Dataset of Speech Production in intracranial Electroencephalography
- Dataset of Speech Production in intracranial Electroencephalography
- Dataset of Speech Production in intracranial Electroencephalography
- Dataset of Speech Production in intracranial Electroencephalography
- Dataset of Speech Production in intracranial Electroencephalography
- Dataset of Speech Production in intracranial Electroencephalography
- Dataset of Speech Production in intracranial Electroencephalography
- Dataset of Speech Production in intracranial Electroencephalography
- Dataset of Speech Production in intracranial Electroencephalography
- Dataset of Speech Production in intracranial Electroencephalography
- Dataset of Speech Production in intracranial Electroencephalography
- Dataset of Speech Production in intracranial Electroencephalography
- Dataset of Speech Production in intracranial Electroencephalography
- Dataset of Speech Production in intracranial Electroencephalography
- Dataset of Speech Production in intracranial Electroencephalography
- Dataset of Speech Production in intracranial Electroencephalography
- Dataset of Speech Production in intracranial Electroencephalography
- Dataset of Speech Production in intracranial Electroencephalography
- Dataset of Speech Production in intracranial Electroencephalography
- Dataset of Speech Production in intracranial Electroencephalography
- Dataset of Speech Production in intracranial Electroencephalography
- Dataset of Speech Production in intracranial Electroencephalography
- Dataset of Speech Production in intracranial Electroencephalography
- Dataset of Speech Production in intracranial Electroencephalography
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Laboratories
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Other :placard: + X
- Neural Interfacing Lab @ Maastricht University
- BCMI Lab @ Shanghai Jiao Tong University
- Safe AI Lab @ Carnegie Mellon University
- NeuroMechatronics Lab @ Carnegie Mellon University
- Interactive Computing Lab @ KAIST
- Laboratory for the Neural Mechanisms of Attention @ UC Davis
- HUman Bio-behavioral Signals Lab @ Texas A&M University
- Computational Intelligence & Neural Engineering Lab @ Hanyang University
- Bioelectronics Laboratory @ Incheon National University
- Healthy ML @ MIT
- Auton Lab @ Carnegie Mellon University
- Biomedical Functional Imaging and Neuroengineering Laboratory @ Carnegie Mellon University
- Laboratoire des Systèmes Perceptifs @ École Normale Supérieure
- Brain and AI Team @ Meta
- Computational Arrhythmia Research Laboratory @ Stanford University
- Wu Tsai Institute @ Yale University
- Shah Lab @ Stanford University
- Edward Yoonjae Choi's Lab @ KAIST
- Scalable Health LAbs @ Rice University
- Pattern Recognition & Machine Learning Lab @ Korea University
- Signal Analysis and Interpretation Lab @ USC
- DataLearning Group @ Imperial College London
- Tison Lab @ UCSF
- Rajpurkar Lab @ Harvard University
- Wang Lab @ University of Toronto
- MAILAB @ Korea University
- Computational Intelligence & Neural Engineering Lab @ Hanyang University
- Computational Arrhythmia Research Laboratory @ Stanford University
- Pattern Recognition & Machine Learning Lab @ Korea University
- MAILAB @ Korea University
- Health AI @ Google
- Health AI @ Apple
- NIAI Lab @ Hanyang University
- Mobile Systems Research Lab @ University of Cambridge
- Aaqib Saeed's Lab @ Eindhoven University of Technology
- Multisensory Intelligence Research Group @ MIT
- Behavioral Data Science Lab @ University of Washington
- AI4Health @ University of Oldenburg
- David Ouyang's Lab @ Cedars-Sinai Medical Center
- Artificial Intelligence in Medicine (AIMed) Lab @ Ohio State University
- Digital Health Lab @ Peking University
- The Alphanumerics Lab @ Georgia Tech and Emory University
- Centre for Brain-Computing Research @ Nanyang Technological University
- SIPLAB @ ETH Zurich
- NIAI Lab @ Hanyang University
- Health Intelligence Lab @ UCLA
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Publications-and-Preprints
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ECG :anatomical_heart: + X
- Automated detection of panic disorder based on multimodal physiological signals using machine learning
- ECGBERT: Understanding Hidden Language of ECGs with Self-Supervised Representation Learning
- ECG Language Processing (ELP): A new technique to analyze ECG signals
- A Novel Algorithm for Movement Artifact Removal in ECG Signals Acquired from Wearable Systems Applied to Horses
- Machine Learning–Enabled Multimodal Fusion of Intra-Atrial and Body Surface Signals in Prediction of Atrial Fibrillation Ablation Outcomes
- ECG and EEG based detection and multilevel classification of stress using machine learning for specified genders: A preliminary study
- Multimodal 12-lead ECG data classification using multi-label DenseNet for heart disease detection
- Time Synchronization of Multimodal Physiological Signals through Alignment of Common Signal Types and Its Technical Considerations in Digital Health
- Robust Patient Information Embedding and Retrieval Mechanism for ECG Signals
- Integrating multimodal information in machine learning for classifying acute myocardial infarction
- Large Language Models for Time Series: A Survey
- Position Paper: What Can Large Language Models Tell Us about Time Series Analysis
- Large Language Model-informed ECG Dual Attention Network for Heart Failure Risk Prediction
- Frozen Language Model Helps ECG Zero-Shot Learning
- Zero-Shot ECG Diagnosis with Large Language Models and Retrieval-Augmented Generation
- A Household Multimodal Physiological Signals Monitoring System
- ECG-QA: A Comprehensive Question Answering Dataset Combined With Electrocardiogram
- Converting ECG Signals to Images for Efficient Image-text Retrieval via Encoding
- ECG Semantic Integrator (ESI): A Foundation ECG Model Pretrained with LLM-Enhanced Cardiological Text
- MEIT: Multi-Modal Electrocardiogram Instruction Tuning on Large Language Models for Report Generation
- Multimodal explainable artificial intelligence identifies patients with non-ischaemic cardiomyopathy at risk of lethal ventricular arrhythmias
- Transfer Knowledge from Natural Language to Electrocardiography: Can We Detect Cardiovascular Disease Through Language Models?
- Position Paper: What Can Large Language Models Tell Us about Time Series Analysis
- Text-to-ECG: 12-Lead Electrocardiogram Synthesis conditioned on Clinical Text Reports
- Deep learning for ECG classification: A comparative study of 1D and 2D representations and multimodal fusion approaches
- Zero-Shot ECG Classification with Multimodal Learning and Test-time Clinical Knowledge Enhancement
- Frozen Language Model Helps ECG Zero-Shot Learning
- ECG Heartbeat Classification Using Multimodal Fusion
- Multimodal ChatGPT-4V for Electrocardiogram Interpretation: Promise and Limitations
- Multimodal ECG heartbeat classification method based on a convolutional neural network embedded with FCA
- Large-scale Training of Foundation Models for Wearable Biosignals
- ECG-Chat: A Large ECG-Language Model for Cardiac Disease Diagnosis
- Multimodal ECG heartbeat classification method based on a convolutional neural network embedded with FCA
- CardioGPT: An ECG Interpretation Generation Model
- Multimodal ECG heartbeat classification method based on a convolutional neural network embedded with FCA
- Harnessing the Power of ChatGPT in Cardiovascular Medicine: Innovations, Challenges, and Future Directions
- Multimodal ECG heartbeat classification method based on a convolutional neural network embedded with FCA
- The potential for large language models to transform cardiovascular medicine - 1/fulltext), The Lancet Digital Health 2024; [ECG, Text]
- Multimodal ECG heartbeat classification method based on a convolutional neural network embedded with FCA
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