• Emotion Recognition from Multimodal Physiological Signals for Emotion Aware Healthcare Systems
• Stress Detection with Deep Learning Approaches Using Physiological Signals
• A Multimodal Approach to Estimating Vigilance Using EEG and Forehead EOG
• Decoding EEG Brain Activity for Multi-Modal Natural Language Processing
• Correlated Attention Networks for Multimodal Emotion Recognition
• Transfer Knowledge from Natural Language to Electrocardiography: Can We Detect Cardiovascular Disease Through Language Models?
• Can Brain Signals Reveal Inner Alignment with Human Languages?
• Advancing NLP with Cognitive Language Processing Signals
• Converting ECG Signals to Images for Efficient Image-text Retrieval via Encoding
• Multimodal Representation Learning of Cardiovascular Magnetic Resonance Imaging
• Recognition of emotions using multimodal physiological signals and an ensemble deep learning model
• Multi-modal emotion analysis from facial expressions and electroencephalogram
• Emotion Recognition from Multimodal Physiological Signals for Emotion Aware Healthcare Systems
• Stress Detection with Deep Learning Approaches Using Physiological Signals
• A Multimodal Approach to Estimating Vigilance Using EEG and Forehead EOG
• Robust Patient Information Embedding and Retrieval Mechanism for ECG Signals
• EmotionMeter: A Multimodal Framework for Recognizing Human Emotions
• A Deep Learning Architecture for Temporal Sleep Stage Classification Using Multivariate and Multimodal Time Series
• Multimodal Physiological Signals and Machine Learning for Stress Detection by Wearable Devices
• Automatic stress detection in car drivers based on non-invasive physiological signals using machine learning techniques
• Hierarchical extreme puzzle learning machine-based emotion recognition using multimodal physiological signals
• Evaluating the Stressful Commutes Using Physiological Signals and Machine Learning Techniques
• Emotion Recognition from Multimodal Physiological Signals for Emotion Aware Healthcare Systems
• Stress Detection with Deep Learning Approaches Using Physiological Signals
• A Multimodal Approach to Estimating Vigilance Using EEG and Forehead EOG
• Automatic stress detection in car drivers based on non-invasive physiological signals using machine learning techniques
• Emotion Recognition from Multimodal Physiological Signals for Emotion Aware Healthcare Systems
• Stress Detection with Deep Learning Approaches Using Physiological Signals
• A Multimodal Approach to Estimating Vigilance Using EEG and Forehead EOG
• Automatic stress detection in car drivers based on non-invasive physiological signals using machine learning techniques
• Negative emotion recognition using multimodal physiological signals for advanced driver assistance systems
• Multimodal Physiological Signal Emotion Recognition Based on Convolutional Recurrent Neural Network
• Emotion Recognition from Multimodal Physiological Signals for Emotion Aware Healthcare Systems
• Stress Detection with Deep Learning Approaches Using Physiological Signals
• A Multimodal Approach to Estimating Vigilance Using EEG and Forehead EOG
• Automatic stress detection in car drivers based on non-invasive physiological signals using machine learning techniques
• Negative emotion recognition using multimodal physiological signals for advanced driver assistance systems
• Text-to-ECG: 12-Lead Electrocardiogram Synthesis conditioned on Clinical Text Reports
• DreamDiffusion: Generating High-Quality Images from Brain EEG Signals
• Correlated Attention Networks for Multimodal Emotion Recognition
• Emotion Recognition from Multimodal Physiological Signals for Emotion Aware Healthcare Systems
• Stress Detection with Deep Learning Approaches Using Physiological Signals
• A Multimodal Approach to Estimating Vigilance Using EEG and Forehead EOG
• EmotionMeter: A Multimodal Framework for Recognizing Human Emotions
• A Deep Learning Architecture for Temporal Sleep Stage Classification Using Multivariate and Multimodal Time Series
• Multimodal Physiological Signals and Machine Learning for Stress Detection by Wearable Devices
• Automatic stress detection in car drivers based on non-invasive physiological signals using machine learning techniques
• Evaluating the Stressful Commutes Using Physiological Signals and Machine Learning Techniques
• Automatic stress detection in car drivers based on non-invasive physiological signals using machine learning techniques
• Negative emotion recognition using multimodal physiological signals for advanced driver assistance systems
• Multimodal Physiological Signal Emotion Recognition Based on Convolutional Recurrent Neural Network
• Emotion Recognition from Multimodal Physiological Signals for Emotion Aware Healthcare Systems
• Stress Detection with Deep Learning Approaches Using Physiological Signals
• Automatic stress detection in car drivers based on non-invasive physiological signals using machine learning techniques
• A Multimodal Approach to Estimating Vigilance Using EEG and Forehead EOG
• Emotion Recognition from Multimodal Physiological Signals for Emotion Aware Healthcare Systems
• Stress Detection with Deep Learning Approaches Using Physiological Signals
• A Multimodal Approach to Estimating Vigilance Using EEG and Forehead EOG
• Automatic stress detection in car drivers based on non-invasive physiological signals using machine learning techniques
• Negative emotion recognition using multimodal physiological signals for advanced driver assistance systems
• Multimodal Physiological Signal Emotion Recognition Based on Convolutional Recurrent Neural Network
• AC-CfC: An attention-based convolutional closed-form continuous-time neural network for raw multi-channel EEG-based emotion recognition
• Correlated Attention Networks for Multimodal Emotion Recognition
• Emotion Recognition from Multimodal Physiological Signals for Emotion Aware Healthcare Systems
• Stress Detection with Deep Learning Approaches Using Physiological Signals
• A Multimodal Approach to Estimating Vigilance Using EEG and Forehead EOG
• EmotionMeter: A Multimodal Framework for Recognizing Human Emotions
• A Deep Learning Architecture for Temporal Sleep Stage Classification Using Multivariate and Multimodal Time Series
• Multimodal Physiological Signals and Machine Learning for Stress Detection by Wearable Devices
• Automatic stress detection in car drivers based on non-invasive physiological signals using machine learning techniques
• Evaluating the Stressful Commutes Using Physiological Signals and Machine Learning Techniques
• Emotion Recognition from Multimodal Physiological Signals for Emotion Aware Healthcare Systems
• Stress Detection with Deep Learning Approaches Using Physiological Signals
• A Multimodal Approach to Estimating Vigilance Using EEG and Forehead EOG
• Automatic stress detection in car drivers based on non-invasive physiological signals using machine learning techniques
• Single-modal and multi-modal false arrhythmia alarm reduction using attention-based convolutional and recurrent neural networks
• Correlated Attention Networks for Multimodal Emotion Recognition
• Emotion Recognition from Multimodal Physiological Signals for Emotion Aware Healthcare Systems
• Stress Detection with Deep Learning Approaches Using Physiological Signals
• A Multimodal Approach to Estimating Vigilance Using EEG and Forehead EOG
• EmotionMeter: A Multimodal Framework for Recognizing Human Emotions
• A Deep Learning Architecture for Temporal Sleep Stage Classification Using Multivariate and Multimodal Time Series
• Multimodal Physiological Signals and Machine Learning for Stress Detection by Wearable Devices
• Automatic stress detection in car drivers based on non-invasive physiological signals using machine learning techniques
• Evaluating the Stressful Commutes Using Physiological Signals and Machine Learning Techniques
• Stress appraisal in the workplace and its associations with productivity and mood: Insights from a multimodal machine learning analysis
• Correlated Attention Networks for Multimodal Emotion Recognition
• Emotion Recognition from Multimodal Physiological Signals for Emotion Aware Healthcare Systems
• Stress Detection with Deep Learning Approaches Using Physiological Signals
• A Multimodal Approach to Estimating Vigilance Using EEG and Forehead EOG
• EmotionMeter: A Multimodal Framework for Recognizing Human Emotions
• A Deep Learning Architecture for Temporal Sleep Stage Classification Using Multivariate and Multimodal Time Series
• Multimodal Physiological Signals and Machine Learning for Stress Detection by Wearable Devices
• Automatic stress detection in car drivers based on non-invasive physiological signals using machine learning techniques
• Evaluating the Stressful Commutes Using Physiological Signals and Machine Learning Techniques
• Emotion Recognition from Multimodal Physiological Signals for Emotion Aware Healthcare Systems
• Stress Detection with Deep Learning Approaches Using Physiological Signals
• A Multimodal Approach to Estimating Vigilance Using EEG and Forehead EOG
• Automatic stress detection in car drivers based on non-invasive physiological signals using machine learning techniques
• Emotion Recognition from Multimodal Physiological Signals for Emotion Aware Healthcare Systems
• Stress Detection with Deep Learning Approaches Using Physiological Signals
• A Multimodal Approach to Estimating Vigilance Using EEG and Forehead EOG
• Automatic stress detection in car drivers based on non-invasive physiological signals using machine learning techniques
• Negative emotion recognition using multimodal physiological signals for advanced driver assistance systems
• Multimodal Physiological Signal Emotion Recognition Based on Convolutional Recurrent Neural Network
• Emotion Recognition from Multimodal Physiological Signals for Emotion Aware Healthcare Systems
• Stress Detection with Deep Learning Approaches Using Physiological Signals
• A Multimodal Approach to Estimating Vigilance Using EEG and Forehead EOG
• Automatic stress detection in car drivers based on non-invasive physiological signals using machine learning techniques
• Emotion Recognition from Multimodal Physiological Signals for Emotion Aware Healthcare Systems
• Stress Detection with Deep Learning Approaches Using Physiological Signals
• A Multimodal Approach to Estimating Vigilance Using EEG and Forehead EOG
• Automatic stress detection in car drivers based on non-invasive physiological signals using machine learning techniques
• Emotion Recognition from Multimodal Physiological Signals for Emotion Aware Healthcare Systems
• Stress Detection with Deep Learning Approaches Using Physiological Signals
• A Multimodal Approach to Estimating Vigilance Using EEG and Forehead EOG
• Automatic stress detection in car drivers based on non-invasive physiological signals using machine learning techniques
• Emotion Recognition from Multimodal Physiological Signals for Emotion Aware Healthcare Systems
• Stress Detection with Deep Learning Approaches Using Physiological Signals
• A Multimodal Approach to Estimating Vigilance Using EEG and Forehead EOG
• Automatic stress detection in car drivers based on non-invasive physiological signals using machine learning techniques
• Stress Detection with Deep Learning Approaches Using Physiological Signals
• Emotion Recognition from Multimodal Physiological Signals for Emotion Aware Healthcare Systems
• A Multimodal Approach to Estimating Vigilance Using EEG and Forehead EOG
• Automatic stress detection in car drivers based on non-invasive physiological signals using machine learning techniques
• Negative emotion recognition using multimodal physiological signals for advanced driver assistance systems
• Multimodal Physiological Signal Emotion Recognition Based on Convolutional Recurrent Neural Network
• Automatic stress detection in car drivers based on non-invasive physiological signals using machine learning techniques
• Emotion Recognition from Multimodal Physiological Signals for Emotion Aware Healthcare Systems
• Stress Detection with Deep Learning Approaches Using Physiological Signals
• A Multimodal Approach to Estimating Vigilance Using EEG and Forehead EOG
• Emotion Recognition from Multimodal Physiological Signals for Emotion Aware Healthcare Systems
• Stress Detection with Deep Learning Approaches Using Physiological Signals
• A Multimodal Approach to Estimating Vigilance Using EEG and Forehead EOG
• Emotion Recognition from Multimodal Physiological Signals for Emotion Aware Healthcare Systems
• Stress Detection with Deep Learning Approaches Using Physiological Signals
• A Multimodal Approach to Estimating Vigilance Using EEG and Forehead EOG
• Automatic stress detection in car drivers based on non-invasive physiological signals using machine learning techniques
• Negative emotion recognition using multimodal physiological signals for advanced driver assistance systems
• Emotion Recognition from Multimodal Physiological Signals for Emotion Aware Healthcare Systems
• Stress Detection with Deep Learning Approaches Using Physiological Signals
• A Multimodal Approach to Estimating Vigilance Using EEG and Forehead EOG
• Automatic stress detection in car drivers based on non-invasive physiological signals using machine learning techniques
• Negative emotion recognition using multimodal physiological signals for advanced driver assistance systems
• Multimodal Physiological Signal Emotion Recognition Based on Convolutional Recurrent Neural Network
• Emotion Recognition from Multimodal Physiological Signals for Emotion Aware Healthcare Systems
• Stress Detection with Deep Learning Approaches Using Physiological Signals
• A Multimodal Approach to Estimating Vigilance Using EEG and Forehead EOG
• Automatic stress detection in car drivers based on non-invasive physiological signals using machine learning techniques
• Emotion Recognition from Multimodal Physiological Signals for Emotion Aware Healthcare Systems
• Stress Detection with Deep Learning Approaches Using Physiological Signals
• A Multimodal Approach to Estimating Vigilance Using EEG and Forehead EOG
• Automatic stress detection in car drivers based on non-invasive physiological signals using machine learning techniques
• Negative emotion recognition using multimodal physiological signals for advanced driver assistance systems
• Multimodal Physiological Signal Emotion Recognition Based on Convolutional Recurrent Neural Network
• Automatic stress detection in car drivers based on non-invasive physiological signals using machine learning techniques
• Negative emotion recognition using multimodal physiological signals for advanced driver assistance systems
• Multimodal Physiological Signal Emotion Recognition Based on Convolutional Recurrent Neural Network
• Stress Detection with Deep Learning Approaches Using Physiological Signals
• A Multimodal Approach to Estimating Vigilance Using EEG and Forehead EOG
• Automatic stress detection in car drivers based on non-invasive physiological signals using machine learning techniques
• Negative emotion recognition using multimodal physiological signals for advanced driver assistance systems
• Multimodal Physiological Signal Emotion Recognition Based on Convolutional Recurrent Neural Network
• Emotion Recognition from Multimodal Physiological Signals for Emotion Aware Healthcare Systems
• Emotion Recognition from Multimodal Physiological Signals for Emotion Aware Healthcare Systems
• Stress Detection with Deep Learning Approaches Using Physiological Signals
• A Multimodal Approach to Estimating Vigilance Using EEG and Forehead EOG
• Automatic stress detection in car drivers based on non-invasive physiological signals using machine learning techniques
• Negative emotion recognition using multimodal physiological signals for advanced driver assistance systems
• Multimodal Physiological Signal Emotion Recognition Based on Convolutional Recurrent Neural Network
• Emotion Recognition from Multimodal Physiological Signals for Emotion Aware Healthcare Systems
• Stress Detection with Deep Learning Approaches Using Physiological Signals
• A Multimodal Approach to Estimating Vigilance Using EEG and Forehead EOG
• Automatic stress detection in car drivers based on non-invasive physiological signals using machine learning techniques
• Negative emotion recognition using multimodal physiological signals for advanced driver assistance systems
• Multimodal Physiological Signal Emotion Recognition Based on Convolutional Recurrent Neural Network
• Emotion Recognition from Multimodal Physiological Signals for Emotion Aware Healthcare Systems
• Stress Detection with Deep Learning Approaches Using Physiological Signals
• A Multimodal Approach to Estimating Vigilance Using EEG and Forehead EOG
• Automatic stress detection in car drivers based on non-invasive physiological signals using machine learning techniques
• Negative emotion recognition using multimodal physiological signals for advanced driver assistance systems
• Multimodal Physiological Signal Emotion Recognition Based on Convolutional Recurrent Neural Network
• Emotion Recognition from Multimodal Physiological Signals for Emotion Aware Healthcare Systems
• Stress Detection with Deep Learning Approaches Using Physiological Signals
• A Multimodal Approach to Estimating Vigilance Using EEG and Forehead EOG
• Automatic stress detection in car drivers based on non-invasive physiological signals using machine learning techniques
• Negative emotion recognition using multimodal physiological signals for advanced driver assistance systems
• Multimodal Physiological Signal Emotion Recognition Based on Convolutional Recurrent Neural Network
• Emotion Recognition from Multimodal Physiological Signals for Emotion Aware Healthcare Systems
• Stress Detection with Deep Learning Approaches Using Physiological Signals
• A Multimodal Approach to Estimating Vigilance Using EEG and Forehead EOG
• Automatic stress detection in car drivers based on non-invasive physiological signals using machine learning techniques
• Negative emotion recognition using multimodal physiological signals for advanced driver assistance systems
• Multimodal Physiological Signal Emotion Recognition Based on Convolutional Recurrent Neural Network
• Emotion Recognition from Multimodal Physiological Signals for Emotion Aware Healthcare Systems
• Stress Detection with Deep Learning Approaches Using Physiological Signals
• A Multimodal Approach to Estimating Vigilance Using EEG and Forehead EOG
• Emotion Recognition from Multimodal Physiological Signals for Emotion Aware Healthcare Systems
• Stress Detection with Deep Learning Approaches Using Physiological Signals
• A Multimodal Approach to Estimating Vigilance Using EEG and Forehead EOG
• Automatic stress detection in car drivers based on non-invasive physiological signals using machine learning techniques
• Negative emotion recognition using multimodal physiological signals for advanced driver assistance systems
• Multimodal Physiological Signal Emotion Recognition Based on Convolutional Recurrent Neural Network
• Emotion Recognition from Multimodal Physiological Signals for Emotion Aware Healthcare Systems
• Stress Detection with Deep Learning Approaches Using Physiological Signals
• A Multimodal Approach to Estimating Vigilance Using EEG and Forehead EOG
• Automatic stress detection in car drivers based on non-invasive physiological signals using machine learning techniques
• Emotion Recognition from Multimodal Physiological Signals for Emotion Aware Healthcare Systems
• Stress Detection with Deep Learning Approaches Using Physiological Signals
• A Multimodal Approach to Estimating Vigilance Using EEG and Forehead EOG
• Automatic stress detection in car drivers based on non-invasive physiological signals using machine learning techniques
• Negative emotion recognition using multimodal physiological signals for advanced driver assistance systems
• Multimodal Physiological Signal Emotion Recognition Based on Convolutional Recurrent Neural Network
• Emotion Recognition from Multimodal Physiological Signals for Emotion Aware Healthcare Systems
• Stress Detection with Deep Learning Approaches Using Physiological Signals
• A Multimodal Approach to Estimating Vigilance Using EEG and Forehead EOG
• Automatic stress detection in car drivers based on non-invasive physiological signals using machine learning techniques
• Negative emotion recognition using multimodal physiological signals for advanced driver assistance systems
• Multimodal Physiological Signal Emotion Recognition Based on Convolutional Recurrent Neural Network
• Negative emotion recognition using multimodal physiological signals for advanced driver assistance systems
• Multimodal Physiological Signal Emotion Recognition Based on Convolutional Recurrent Neural Network
• Negative emotion recognition using multimodal physiological signals for advanced driver assistance systems
• Multimodal Physiological Signal Emotion Recognition Based on Convolutional Recurrent Neural Network

EEG :brain: + X

• Can Brain Signals Reveal Inner Alignment with Human Languages?
• DeWave: Discrete EEG Waves Encoding for Brain Dynamics to Text Translation
• Temporal-Spatial Prediction: Pre-Training on Diverse Datasets for EEG Classification
• Joint Contrastive Learning with Feature Alignment for Cross-Corpus EEG-based Emotion Recognition
• EEGFormer: Towards Transferable and Interpretable Large-Scale EEG Foundation Model
• An ASR-based Hybrid Approach for Auditory Attention Decoding
• Evaluating the Stressful Commutes Using Physiological Signals and Machine Learning Techniques
• DPD (DePression Detection) Net: a deep neural network for multimodal depression detection
• Review of Machine and Deep Learning Techniques in Epileptic Seizure Detection using Physiological Signals and Sentiment Analysis
• Evaluating the Stressful Commutes Using Physiological Signals and Machine Learning Techniques
• EEG-Language Modeling for Pathology Detection
• SEE: Semantically Aligned EEG-to-Text Translation
• EEGPT: Unleashing the Potential of EEG Generalist Foundation Model by Autoregressive Pre-training
• NeuroLM: A Universal Multi-task Foundation Model for Bridging the Gap between Language and EEG Signals
• Are EEG-to-Text Models Working?
• Aligning Semantic in Brain and Language: A Curriculum Contrastive Method for Electroencephalography-to-Text Generation
• EmotionKD: A Cross-Modal Knowledge Distillation Framework for Emotion Recognition Based on Physiological Signals
• Deep-learning models reveal how context and listener attention shape electrophysiological correlates of speech-to-language transformation
• ARIEL: Brain-Computer Interfaces meet Large Language Models for Emotional Support Conversation
• DPD (DePression Detection) Net: a deep neural network for multimodal depression detection
• Position: Addressing Ethical Challenges and Safety Risks in GenAI-Powered Brain-Computer Interfaces
• A Hybrid Artificial Intelligence System for Automated EEG Background Analysis and Report Generation
• Recognition of emotions using multimodal physiological signals and an ensemble deep learning model
• Multi-modal emotion analysis from facial expressions and electroencephalogram
• AC-CfC: An attention-based convolutional closed-form continuous-time neural network for raw multi-channel EEG-based emotion recognition
• Neural Speech Tracking in EEG: Integrating Acoustics and Linguistics for Hearing Aid Users
• LLM-enhanced Multi-teacher Knowledge Distillation for Modality-Incomplete Emotion Recognition in Daily Healthcare
• A Classification Model for Sensing Human Trust in Machines Using EEG and GSR
• Open Vocabulary Electroencephalography-to-Text Decoding and Zero-Shot Sentiment Classification
• Emotion recognition based on multi-modal physiological signals and transfer learning - Plethysmograph]
• Multimodal Affective States Recognition Based on Multiscale CNNs and Biologically Inspired Decision Fusion Model
• Language Mapping using tEEG and EEG Data with Convolutional Neural Networks
• Understanding language-elicited EEG data by predicting it from a fine-tuned language model
• Evaluating the Stressful Commutes Using Physiological Signals and Machine Learning Techniques
• EEG-GPT: Exploring Capabilities of Large Language Models for EEG Classification and Interpretation
• Large Brain Model for Learning Generic Representations with Tremendous EEG Data in BCI
• EEG-Transformer: Self-attention from transformer architecture for decoding EEG of imagined speech
• Towards Voice Reconstruction from EEG during Imagined Speech
• DPD (DePression Detection) Net: a deep neural network for multimodal depression detection
• Decoding text from electroencephalography signals: A novel Hierarchical Gated Recurrent Unit with Masked Residual Attention Mechanism
• Towards Neural Foundation Models for Vision: Aligning EEG, MEG, and fMRI Representations for Decoding, Encoding, and Modality Conversion
• Nested Deep Learning Model Towards A Foundation Model for Brain Signal Data
• EEG Emotion Copilot: Pruning LLMs for Emotional EEG Interpretation with Assisted Medical Record Generation
• Thought2Text: Text Generation from EEG Signal using Large Language Models (LLMs)
• Toward Fully-End-to-End Listened Speech Decoding from EEG Signals
• Enhancing Affective Representations Of Music-Induced Eeg Through Multimodal Supervision And Latent Domain Adaptation
• EEG2TEXT: Open Vocabulary EEG-to-Text Decoding with EEG Pre-Training and Multi-View Transformer
• Scaling Law in Neural Data: Non-Invasive Speech Decoding with 175 Hours of EEG Data
• An ASR-based Hybrid Approach for Auditory Attention Decoding
• Neural Speech Tracking in EEG: Integrating Acoustics and Linguistics for Hearing Aid Users
• A Multimodal Approach to Estimating Vigilance Using EEG and Forehead EOG
• Negative emotion recognition using multimodal physiological signals for advanced driver assistance systems
• Advancing NLP with Cognitive Language Processing Signals
• DreamDiffusion: Generating High-Quality Images from Brain EEG Signals
• EEG-Based Multimodal Emotion Recognition: A Machine Learning Perspective
• DPD (DePression Detection) Net: a deep neural network for multimodal depression detection

EOG :eye: + X

• A Multimodal Approach to Estimating Vigilance Using EEG and Forehead EOG
• Machine-Learning-Based Detection of Craving for Gaming Using Multimodal Physiological Signals: Validation of Test-Retest Reliability for Practical Use
• A Multimodal Approach to Estimating Vigilance Using EEG and Forehead EOG
• A Multimodal Approach to Estimating Vigilance Using EEG and Forehead EOG
• A Multimodal Approach to Estimating Vigilance Using EEG and Forehead EOG
• A Multimodal Approach to Estimating Vigilance Using EEG and Forehead EOG
• A Multimodal Approach to Estimating Vigilance Using EEG and Forehead EOG
• A Multimodal Approach to Estimating Vigilance Using EEG and Forehead EOG
• A Multimodal Approach to Estimating Vigilance Using EEG and Forehead EOG
• A Multimodal Approach to Estimating Vigilance Using EEG and Forehead EOG
• A Multimodal Approach to Estimating Vigilance Using EEG and Forehead EOG
• A Multimodal Approach to Estimating Vigilance Using EEG and Forehead EOG
• A Multimodal Approach to Estimating Vigilance Using EEG and Forehead EOG
• A Multimodal Approach to Estimating Vigilance Using EEG and Forehead EOG
• A Multimodal Approach to Estimating Vigilance Using EEG and Forehead EOG
• A Multimodal Approach to Estimating Vigilance Using EEG and Forehead EOG
• A Multimodal Approach to Estimating Vigilance Using EEG and Forehead EOG
• A Multimodal Approach to Estimating Vigilance Using EEG and Forehead EOG
• A Multimodal Approach to Estimating Vigilance Using EEG and Forehead EOG
• A Multimodal Approach to Estimating Vigilance Using EEG and Forehead EOG
• A Multimodal Approach to Estimating Vigilance Using EEG and Forehead EOG
• A Multimodal Approach to Estimating Vigilance Using EEG and Forehead EOG
• A Multimodal Approach to Estimating Vigilance Using EEG and Forehead EOG
• A Multimodal Approach to Estimating Vigilance Using EEG and Forehead EOG
• A Multimodal Approach to Estimating Vigilance Using EEG and Forehead EOG
• A Multimodal Approach to Estimating Vigilance Using EEG and Forehead EOG
• A Multimodal Approach to Estimating Vigilance Using EEG and Forehead EOG
• Cognitive Load Prediction from Multimodal Physiological Signals using Multiview Learning.
• A Multimodal Approach to Estimating Vigilance Using EEG and Forehead EOG
• A Multimodal Approach to Estimating Vigilance Using EEG and Forehead EOG
• Validation and Interpretation of a Multimodal Drowsiness Detection System Using Explainable Machine Learning
• A Multimodal Approach to Estimating Vigilance Using EEG and Forehead EOG

EDA :sweat_drops: + X

• Negative emotion recognition using multimodal physiological signals for advanced driver assistance systems
• Stress Detection with Deep Learning Approaches Using Physiological Signals
• A Hybrid Deep Learning Emotion Classification System Using Multimodal Data
• Transformer-based Self-supervised Multimodal Representation Learning for Wearable Emotion Recognition
• Effects of Physiological Signals in Different Types of Multimodal Sentiment Estimation
• A Multimodal Music Recommendation System with Listeners' Personality and Physiological Signals
• Negative emotion recognition using multimodal physiological signals for advanced driver assistance systems
• Stress Detection with Deep Learning Approaches Using Physiological Signals
• Stress Detection with Deep Learning Approaches Using Physiological Signals
• Negative emotion recognition using multimodal physiological signals for advanced driver assistance systems
• Multimodal Physiological Signals and Machine Learning for Stress Detection by Wearable Devices
• Negative emotion recognition using multimodal physiological signals for advanced driver assistance systems
• Stress Detection with Deep Learning Approaches Using Physiological Signals
• Multimodal Physiological Signals and Machine Learning for Stress Detection by Wearable Devices
• Negative emotion recognition using multimodal physiological signals for advanced driver assistance systems
• Stress Detection with Deep Learning Approaches Using Physiological Signals
• Stress Detection with Deep Learning Approaches Using Physiological Signals
• Negative emotion recognition using multimodal physiological signals for advanced driver assistance systems
• Stress Detection with Deep Learning Approaches Using Physiological Signals
• Negative emotion recognition using multimodal physiological signals for advanced driver assistance systems
• Stress Detection with Deep Learning Approaches Using Physiological Signals
• Transformer-Based Physiological Feature Learning for Multimodal Analysis of Self-Reported Sentiment
• Exploring the Potential of Multimodal Emotion Recognition for Hearing-Impaired Children Using Physiological Signals and Facial Expressions
• Performance Exploration of RNN Variants for Recognizing Daily Life Stress Levels by Using Multimodal Physiological Signals
• Negative emotion recognition using multimodal physiological signals for advanced driver assistance systems
• Negative emotion recognition using multimodal physiological signals for advanced driver assistance systems
• Stress Detection with Deep Learning Approaches Using Physiological Signals
• Stress Detection with Deep Learning Approaches Using Physiological Signals
• Negative emotion recognition using multimodal physiological signals for advanced driver assistance systems
• Multimodal Physiological Signals and Machine Learning for Stress Detection by Wearable Devices
• Stress Detection with Deep Learning Approaches Using Physiological Signals
• Negative emotion recognition using multimodal physiological signals for advanced driver assistance systems
• Negative emotion recognition using multimodal physiological signals for advanced driver assistance systems
• Stress Detection with Deep Learning Approaches Using Physiological Signals
• Negative emotion recognition using multimodal physiological signals for advanced driver assistance systems
• Stress Detection with Deep Learning Approaches Using Physiological Signals
• Negative emotion recognition using multimodal physiological signals for advanced driver assistance systems
• Stress Detection with Deep Learning Approaches Using Physiological Signals
• Stress Detection with Deep Learning Approaches Using Physiological Signals
• Negative emotion recognition using multimodal physiological signals for advanced driver assistance systems
• Negative emotion recognition using multimodal physiological signals for advanced driver assistance systems
• Stress Detection with Deep Learning Approaches Using Physiological Signals
• Negative emotion recognition using multimodal physiological signals for advanced driver assistance systems
• Stress Detection with Deep Learning Approaches Using Physiological Signals
• An Exploratory Study of Multimodal Physiological Data in Jazz Improvisation Using Basic Machine Learning Techniques
• Stress Detection with Deep Learning Approaches Using Physiological Signals
• Negative emotion recognition using multimodal physiological signals for advanced driver assistance systems
• Negative emotion recognition using multimodal physiological signals for advanced driver assistance systems
• Hierarchical extreme puzzle learning machine-based emotion recognition using multimodal physiological signals
• Stress Detection with Deep Learning Approaches Using Physiological Signals
• Stress Detection with Deep Learning Approaches Using Physiological Signals
• Negative emotion recognition using multimodal physiological signals for advanced driver assistance systems
• Negative emotion recognition using multimodal physiological signals for advanced driver assistance systems
• Stress Detection with Deep Learning Approaches Using Physiological Signals
• Negative emotion recognition using multimodal physiological signals for advanced driver assistance systems
• Stress Detection with Deep Learning Approaches Using Physiological Signals
• Negative emotion recognition using multimodal physiological signals for advanced driver assistance systems
• Stress Detection with Deep Learning Approaches Using Physiological Signals
• Stress Detection with Deep Learning Approaches Using Physiological Signals
• Negative emotion recognition using multimodal physiological signals for advanced driver assistance systems
• Negative emotion recognition using multimodal physiological signals for advanced driver assistance systems
• A Framework for Cognitive Load Recognition Based on Machine Learning and Multimodal Physiological Signals by Wearable Sensors
• Stress Detection with Deep Learning Approaches Using Physiological Signals
• Negative emotion recognition using multimodal physiological signals for advanced driver assistance systems
• Stress Detection with Deep Learning Approaches Using Physiological Signals
• Negative emotion recognition using multimodal physiological signals for advanced driver assistance systems
• Stress Detection with Deep Learning Approaches Using Physiological Signals
• Negative emotion recognition using multimodal physiological signals for advanced driver assistance systems
• Stress Detection with Deep Learning Approaches Using Physiological Signals
• Stress Detection with Deep Learning Approaches Using Physiological Signals
• Negative emotion recognition using multimodal physiological signals for advanced driver assistance systems
• Automated detection of panic disorder based on multimodal physiological signals using machine learning

EMG :muscle: + X

• Multimodal Physiological Signal Emotion Recognition Based on Convolutional Recurrent Neural Network
• Automatic stress detection in car drivers based on non-invasive physiological signals using machine learning techniques
• Multimodal Physiological Signal Emotion Recognition Based on Convolutional Recurrent Neural Network
• Automatic stress detection in car drivers based on non-invasive physiological signals using machine learning techniques
• Multimodal Physiological Signal Emotion Recognition Based on Convolutional Recurrent Neural Network
• Automatic stress detection in car drivers based on non-invasive physiological signals using machine learning techniques
• Multimodal Physiological Signal Emotion Recognition Based on Convolutional Recurrent Neural Network
• Automatic stress detection in car drivers based on non-invasive physiological signals using machine learning techniques
• EMG-to-Speech: Direct Generation of Speech From Facial Electromyographic Signals
• Automatic stress detection in car drivers based on non-invasive physiological signals using machine learning techniques
• Multimodal Physiological Signal Emotion Recognition Based on Convolutional Recurrent Neural Network
• Multimodal Physiological Signal Emotion Recognition Based on Convolutional Recurrent Neural Network
• Automatic stress detection in car drivers based on non-invasive physiological signals using machine learning techniques
• Automatic stress detection in car drivers based on non-invasive physiological signals using machine learning techniques
• Multimodal Physiological Signal Emotion Recognition Based on Convolutional Recurrent Neural Network
• A Deep Learning Architecture for Temporal Sleep Stage Classification Using Multivariate and Multimodal Time Series
• Multimodal Physiological Signal Emotion Recognition Based on Convolutional Recurrent Neural Network
• Embracing Large Language and Multimodal Models for Prosthetic Technologies
• Automatic stress detection in car drivers based on non-invasive physiological signals using machine learning techniques
• Multimodal Physiological Signal Emotion Recognition Based on Convolutional Recurrent Neural Network
• Automatic stress detection in car drivers based on non-invasive physiological signals using machine learning techniques
• Multimodal Physiological Signal Emotion Recognition Based on Convolutional Recurrent Neural Network
• Automatic stress detection in car drivers based on non-invasive physiological signals using machine learning techniques
• A Deep Learning Architecture for Temporal Sleep Stage Classification Using Multivariate and Multimodal Time Series
• Multimodal Physiological Signal Emotion Recognition Based on Convolutional Recurrent Neural Network
• Automatic stress detection in car drivers based on non-invasive physiological signals using machine learning techniques
• Multimodal Physiological Signal Emotion Recognition Based on Convolutional Recurrent Neural Network
• Automatic stress detection in car drivers based on non-invasive physiological signals using machine learning techniques
• Multimodal Physiological Signal Emotion Recognition Based on Convolutional Recurrent Neural Network
• Automatic stress detection in car drivers based on non-invasive physiological signals using machine learning techniques
• Multimodal Physiological Signal Emotion Recognition Based on Convolutional Recurrent Neural Network
• Automatic stress detection in car drivers based on non-invasive physiological signals using machine learning techniques
• Automatic stress detection in car drivers based on non-invasive physiological signals using machine learning techniques
• Multimodal Physiological Signal Emotion Recognition Based on Convolutional Recurrent Neural Network
• Comparative analysis of physiological signals and electroencephalogram (EEG) for multimodal emotion recognition using generative models
• Multimodal Physiological Signal Emotion Recognition Based on Convolutional Recurrent Neural Network
• Automatic stress detection in car drivers based on non-invasive physiological signals using machine learning techniques
• Multimodal Physiological Signal Emotion Recognition Based on Convolutional Recurrent Neural Network
• Automatic stress detection in car drivers based on non-invasive physiological signals using machine learning techniques
• Multimodal Physiological Signal Emotion Recognition Based on Convolutional Recurrent Neural Network
• Learning under Label Noise through Few-Shot Human-in-the-Loop Refinement
• Automatic stress detection in car drivers based on non-invasive physiological signals using machine learning techniques
• Multimodal Physiological Signal Emotion Recognition Based on Convolutional Recurrent Neural Network
• Automatic stress detection in car drivers based on non-invasive physiological signals using machine learning techniques
• A Deep Learning Architecture for Temporal Sleep Stage Classification Using Multivariate and Multimodal Time Series
• Multimodal Physiological Signal Emotion Recognition Based on Convolutional Recurrent Neural Network
• Automatic stress detection in car drivers based on non-invasive physiological signals using machine learning techniques
• Multimodal Physiological Signal Emotion Recognition Based on Convolutional Recurrent Neural Network
• Automatic stress detection in car drivers based on non-invasive physiological signals using machine learning techniques
• Multimodal Physiological Signal Emotion Recognition Based on Convolutional Recurrent Neural Network
• PedSleepMAE: Generative Model for Multimodal Pediatric Sleep Signals
• Automatic stress detection in car drivers based on non-invasive physiological signals using machine learning techniques
• Multimodal Physiological Signal Emotion Recognition Based on Convolutional Recurrent Neural Network
• Automatic stress detection in car drivers based on non-invasive physiological signals using machine learning techniques
• Multimodal Physiological Signal Emotion Recognition Based on Convolutional Recurrent Neural Network
• Automatic stress detection in car drivers based on non-invasive physiological signals using machine learning techniques
• Multimodal Estimation Of Change Points Of Physiological Arousal During Driving
• Multimodal Physiological Signal Emotion Recognition Based on Convolutional Recurrent Neural Network
• Automatic stress detection in car drivers based on non-invasive physiological signals using machine learning techniques
• Multimodal Physiological Signal Emotion Recognition Based on Convolutional Recurrent Neural Network
• Automatic stress detection in car drivers based on non-invasive physiological signals using machine learning techniques
• Multimodal Physiological Signal Emotion Recognition Based on Convolutional Recurrent Neural Network
• Automatic stress detection in car drivers based on non-invasive physiological signals using machine learning techniques
• Multimodal Physiological Signal Emotion Recognition Based on Convolutional Recurrent Neural Network
• Automatic stress detection in car drivers based on non-invasive physiological signals using machine learning techniques
• Multimodal Physiological Signal Emotion Recognition Based on Convolutional Recurrent Neural Network
• SleepFM: Multi-modal Representation Learning for Sleep Across Brain Activity, ECG and Respiratory Signals
• Promoting cross-modal representations to improve multimodal foundation models for physiological signals - FM) workshop at NeurIPS 2024; [EEG, EMG, EOG, ECG]
• Automatic stress detection in car drivers based on non-invasive physiological signals using machine learning techniques
• Multi-Signal Reconstruction Using Masked Autoencoder From EEG During Polysomnography
• A generative foundation model for five-class sleep staging with arbitrary sensor input

Other :placard: + X

• Emotion Recognition from Multimodal Physiological Signals for Emotion Aware Healthcare Systems
• Biometric Recognition Using Multimodal Physiological Signals
• Video-based multimodal spontaneous emotion recognition using facial expressions and physiological signals
• Every word counts: A multilingual analysis of individual human alignment with model attention - Tracking, Language]
• Early Life Stress Detection Using Physiological Signals and Machine Learning Pipelines
• Comparative Analysis of Emotion Classification Based on Facial Expression and Physiological Signals Using Deep Learning
• Emotion Recognition From Multimodal Physiological Signals via Discriminative Correlation Fusion With a Temporal Alignment Mechanism
• Multimodal Risk Prediction with Physiological Signals, Medical Images and Clinical Notes
• Emotion Recognition from Multimodal Physiological Signals for Emotion Aware Healthcare Systems
• Emotion Recognition from Multimodal Physiological Signals for Emotion Aware Healthcare Systems
• SIM-CNN: Self-Supervised Individualized Multimodal Learning for Stress Prediction on Nurses Using Biosignals
• Emotion Recognition from Multimodal Physiological Signals for Emotion Aware Healthcare Systems
• PhysioMTL: Personalizing Physiological Patterns using Optimal Transport Multi-Task Regression
• Emotion Recognition from Multimodal Physiological Signals for Emotion Aware Healthcare Systems
• Emotion Recognition from Multimodal Physiological Signals for Emotion Aware Healthcare Systems
• Emotion Recognition from Multimodal Physiological Signals for Emotion Aware Healthcare Systems
• LLM-ABBA: Understand time series via symbolic approximation
• Emotion Recognition from Multimodal Physiological Signals for Emotion Aware Healthcare Systems
• Towards Autonomous Physiological Signal Extraction From Thermal Videos Using Deep Learning
• Emotion Recognition from Multimodal Physiological Signals for Emotion Aware Healthcare Systems
• Interpretation of Intracardiac Electrograms Through Textual Representations
• Emotion Recognition from Multimodal Physiological Signals for Emotion Aware Healthcare Systems
• Emotion Recognition from Multimodal Physiological Signals for Emotion Aware Healthcare Systems
• Emotion Recognition from Multimodal Physiological Signals for Emotion Aware Healthcare Systems
• Do Self-Supervised Speech and Language Models Extract Similar Representations as Human Brain?
• Emotion Recognition from Multimodal Physiological Signals for Emotion Aware Healthcare Systems
• Emotion Recognition from Multimodal Physiological Signals for Emotion Aware Healthcare Systems
• Towards a Personal Health Large Language Model
• Emotion Recognition from Multimodal Physiological Signals for Emotion Aware Healthcare Systems
• TOTEM: TOkenized T ime Series EMbeddings for General Time Series Analysis
• Evaluating Large Language Models on Time Series Feature Understanding: A Comprehensive Taxonomy and Benchmark
• Emotion Recognition from Multimodal Physiological Signals for Emotion Aware Healthcare Systems
• Transformers in biosignal analysis: A review
• Emotion Recognition from Multimodal Physiological Signals for Emotion Aware Healthcare Systems
• Emotion Recognition from Multimodal Physiological Signals for Emotion Aware Healthcare Systems
• PaPaGei: Open Foundation Models for Optical Physiological Signals
• Multi-Modal Forecaster: Jointly Predicting Time Series and Textual Data
• Emotion Recognition from Multimodal Physiological Signals for Emotion Aware Healthcare Systems
• Transformers in biosignal analysis: A review
• Brant: Foundation Model for Intracranial Neural Signal
• BrainBERT: Self-supervised representation learning for intracranial recordings
• Emotion Recognition from Multimodal Physiological Signals for Emotion Aware Healthcare Systems
• Emotion Recognition from Multimodal Physiological Signals for Emotion Aware Healthcare Systems
• Event-Based Contrastive Learning for Medical Time Series
• Emotion Recognition from Multimodal Physiological Signals for Emotion Aware Healthcare Systems
• COCOA: Cross Modality Contrastive Learning for Sensor Data
• Language Models Still Struggle to Zero-shot Reason about Time Series
• TimeSeriesExam: A time series understanding exam
• Emotion Recognition from Multimodal Physiological Signals for Emotion Aware Healthcare Systems
• Multimodal Representation Learning of Cardiovascular Magnetic Resonance Imaging
• Emotion Recognition from Multimodal Physiological Signals for Emotion Aware Healthcare Systems
• Emotion Recognition from Multimodal Physiological Signals for Emotion Aware Healthcare Systems
• Health-LLM: Large Language Models for Health Prediction via Wearable Sensor Data
• Emotion Recognition from Multimodal Physiological Signals for Emotion Aware Healthcare Systems
• MAD: Multi-Alignment MEG-to-Text Decoding
• BTS: Bridging Text and Sound Modalities for Metadata-Aided Respiratory Sound Classification
• Emotion Recognition from Multimodal Physiological Signals for Emotion Aware Healthcare Systems
• Emotion Recognition from Multimodal Physiological Signals for Emotion Aware Healthcare Systems
• BIOT: Biosignal Transformer for Cross-data Learning in the Wild
• Emotion Recognition from Multimodal Physiological Signals for Emotion Aware Healthcare Systems

ECG :anatomical_heart: + X

• Frozen Language Model Helps ECG Zero-Shot Learning
• 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
• ECG-QA: A Comprehensive Question Answering Dataset Combined With Electrocardiogram
• Comparing Recognition Performance and Robustness of Multimodal Deep Learning Models for Multimodal Emotion Recognition
• Multimodal 12-lead ECG data classification using multi-label DenseNet for heart disease detection
• Multimodal ECG heartbeat classification method based on a convolutional neural network embedded with FCA
• Multimodal ECG heartbeat classification method based on a convolutional neural network embedded with FCA
• Time Synchronization of Multimodal Physiological Signals through Alignment of Common Signal Types and Its Technical Considerations in Digital Health
• 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
• C-MELT: Contrastive Enhanced Masked Auto-Encoders for ECG-Language Pre-Training
• Let Your Heart Speak in its Mother Tongue: Multilingual Captioning of Cardiac Signals
• Multimodal ECG heartbeat classification method based on a convolutional neural network embedded with FCA
• Cross-Modality Cardiac Insight Transfer: A Contrastive Learning Approach to Enrich ECG with CMR Features
• Multimodal ECG heartbeat classification method based on a convolutional neural network embedded with FCA
• Explainable Artificial Intelligence on Biosignals for Clinical Decision Support
• 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
• ECG Heartbeat Classification Using Multimodal Fusion
• Multimodal ECG heartbeat classification method based on a convolutional neural network embedded with FCA
• CardioGPT: An ECG Interpretation Generation Model
• An Ensemble Learning Method for Emotion Charting Using Multimodal Physiological Signals
• Multimodal ChatGPT-4V for Electrocardiogram Interpretation: Promise and Limitations
• Recent Trends of Multimodal Affective Computing: A Survey from NLP Perspective
• Multimodal ECG heartbeat classification method based on a convolutional neural network embedded with FCA
• 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
• 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
• Foundation Models for Cardiovascular Disease Detection via BioSignals from Digital Stethoscopes
• Multimodal ECG heartbeat classification method based on a convolutional neural network embedded with FCA
• Electrocardiogram–Language Model for Few-Shot Question Answering with Meta Learning
• Multimodal ECG heartbeat classification method based on a convolutional neural network embedded with FCA
• Electrocardiogram Report Generation and Question Answering via Retrieval-Augmented Self-Supervised Modeling
• 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]
• Converting ECG Signals to Images for Efficient Image-text Retrieval via Encoding
• A Household Multimodal Physiological Signals Monitoring System
• Multimodal ECG heartbeat classification method based on a convolutional neural network embedded with FCA
• Precision of artificial intelligence in paediatric cardiology multimodal image interpretation
• Multimodal ECG heartbeat classification method based on a convolutional neural network embedded with FCA
• ECG and EEG based detection and multilevel classification of stress using machine learning for specified genders: A preliminary study
• Machine Learning–Enabled Multimodal Fusion of Intra-Atrial and Body Surface Signals in Prediction of Atrial Fibrillation Ablation Outcomes
• Stress Classification by Multimodal Physiological Signals Using Variational Mode Decomposition and Machine Learning
• A Novel Algorithm for Movement Artifact Removal in ECG Signals Acquired from Wearable Systems Applied to Horses
• 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
• Multimodal ECG heartbeat classification method based on a convolutional neural network embedded with FCA
• Foundation Models in Electrocardiogram: A Review
• A Survey of LLMs on Biosignal Applications
• Multimodal ECG heartbeat classification method based on a convolutional neural network embedded with FCA
• HeartBERT: A Self-Supervised ECG Embedding Model for Efficient and Effective Medical Signal Analysis
• Multimodal ECG heartbeat classification method based on a convolutional neural network embedded with FCA
• From Hospital to Portables: A Universal ECG Foundation Model Built on 10+ Million Diverse Recordings
• ECG Semantic Integrator (ESI): A Foundation ECG Model Pretrained with LLM-Enhanced Cardiological Text
• Multimodal ECG heartbeat classification method based on a convolutional neural network embedded with FCA
• Physiological Fusion Net: Quantifying Individual VR Sickness with Content Stimulus and Physiological Response
• 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
• Multimodal ECG heartbeat classification method based on a convolutional neural network embedded with FCA
• De-biased Multimodal Electrocardiogram Analysis
• Cross-Modality Cardiac Insight Transfer: A Contrastive Learning Approach to Enrich ECG with CMR Features
• Large-scale cross-modality pretrained model enhances cardiovascular state estimation and cardiomyopathy detection from electrocardiograms: An AI system development and multi-center validation study
• Multimodal Physiological Signals Fusion for Online Emotion Recognition
• Modeling Multivariate Biosignals With Graph Neural Networks and Structured State Space Models
• An Efficient Multimodal Emotion Identification Using FOX Optimized Double Deep Q-Learning
• Large Language Models for Time Series: A Survey
• Hypercomplex Multimodal Emotion Recognition from EEG and Peripheral Physiological Signals

Eye Movement :eye: + X

• Correlated Attention Networks for Multimodal Emotion Recognition
• EmotionMeter: A Multimodal Framework for Recognizing Human Emotions
• Correlated Attention Networks for Multimodal Emotion Recognition
• Multimodal Emotion Recognition Using Deep Neural Networks
• Multimodal Adaptive Emotion Transformer with Flexible Modality Inputs on A Novel Dataset with Continuous Labels
• Correlated Attention Networks for Multimodal Emotion Recognition
• EmotionMeter: A Multimodal Framework for Recognizing Human Emotions
• Navigating Brain Language Representations: A Comparative Analysis of Neural Language Models and Psychologically Plausible Models
• Emotion Transformer Fusion: Complementary Representation Properties of EEG and Eye Movements on Recognizing Anger and Surprise
• A Multitask Framework for Emotion Recognition Using EEG and Eye Movement Signals with Adversarial Training and Attention Mechanism
• Investigating Sex Differences in Classification of Five Emotions from EEG and Eye Movement Signals
• Multi-view Emotion Recognition Using Deep Canonical Correlation Analysis
• EEG2Video: Towards Decoding Dynamic Visual Perception from EEG Signals
• Functional Emotion Transformer for EEG-Assisted Cross-Modal Emotion Recognition
• Graph to Grid: Learning Deep Representations for Multimodal Emotion Recognition
• EmotionMeter: A Multimodal Framework for Recognizing Human Emotions
• Naturalistic Emotion Recognition Using EEG and Eye Movements
• Multimodal Multi-View Spectral-Spatial-Temporal Masked Autoencoder for Self-Supervised Emotion Recognition

Other :placard: + X

• Dataset of Speech Production in intracranial Electroencephalography
• ECG-QA
• SEED
• SEED-IV
• SEED-VIG
• SEED-V
• AMIGOS
• A multi-modal open dataset for mental-disorder analysis
• Sleep-EDF
• Neurosity EEG Dataset
• Dataset of Speech Production in intracranial Electroencephalography
• EEG-ImageNet: An Electroencephalogram Dataset and Benchmarks with Image Visual Stimuli of Multi-Granularity Labels
• Temple University Hospital EEG Seizure Corpus
• 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
• DEAP
• Dataset of Speech Production in intracranial Electroencephalography
• The Dutch EEG Speech Register Corpus
• Dataset of Speech Production in intracranial Electroencephalography
• Dataset of Speech Production in intracranial Electroencephalography
• SEED-GER
• SEED-FRA
• ZuCo 2.0
• Dataset of Speech Production in intracranial Electroencephalography
• Alljoined1 - A dataset for EEG-to-Image decoding
• MindBigData
• Dataset of Speech Production in intracranial Electroencephalography
• EIT-1M: One Million EEG-Image-Text Pairs for Human Visual-textual Recognition and More
• 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
• A Large and Rich EEG Dataset for Modeling Human Visual Object Recognition
• Dataset of Speech Production in intracranial Electroencephalography
• Human EEG recordings for 1,854 concepts presented in rapid serial visual presentation streams

• DEAP
• 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
• 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
• 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

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
• Shah Lab @ Stanford University
• Edward Yoonjae Choi's Lab @ KAIST
• Laboratoire des Systèmes Perceptifs @ École Normale Supérieure
• Brain and AI Team @ Meta
• Computational Arrhythmia Research Laboratory @ Stanford University
• Wu Tsai Institute @ Yale University
• Tison Lab @ UCSF
• Rajpurkar Lab @ Harvard University
• Wang Lab @ University of Toronto
• MAILAB @ Korea University
• 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
• Pattern Recognition & Machine Learning Lab @ Korea University
• Signal Analysis and Interpretation Lab @ USC
• Scalable Health LAbs @ Rice University
• DataLearning Group @ Imperial College London

• Centre for Language Technology @ University of Copenhagen