EEG and Brain-Computer Interfaces
100 papers on this topic
CognitionCapturer: Decoding Visual Stimuli From Human EEG Signal With Multimodal Information
Electroencephalogram (EEG) signals have attracted significant attention from researchers due to their non-invasive nature and high temporal sensitivity in decoding visual stimuli. However, most...
MindBridge: A Cross-Subject Brain Decoding Framework
Brain decoding, a pivotal field in neuroscience, aims to reconstruct stimuli from acquired brain signals, primarily utilizing functional magnetic resonance imaging (fMRI). Currently, brain...
NeuroChat: A Neuroadaptive AI Chatbot for Customizing Learning Experiences
Generative AI is transforming education by enabling personalized, on-demand learning experiences. However, current AI systems lack awareness of the learner's cognitive state, limiting their...
Large Language Models for EEG: A Comprehensive Survey and Taxonomy
The growing convergence between Large Language Models (LLMs) and electroencephalography (EEG) research is enabling new directions in neural decoding, brain-computer interfaces (BCIs), and...
A Systematic Review on Sleep Stage Classification and Sleep Disorder Detection Using Artificial Intelligence
Sleep is vital for people's physical and mental health, and sound sleep can help them focus on daily activities. Therefore, a sleep study that includes sleep patterns and sleep disorders is...
GMSS: Graph-Based Multi-Task Self-Supervised Learning for EEG Emotion Recognition
Previous electroencephalogram (EEG) emotion recognition relies on single-task learning, which may lead to overfitting and learned emotion features lacking generalization. In this paper, a...
EEG-SSM: Leveraging State-Space Model for Dementia Detection
State-space models (SSMs) have garnered attention for effectively processing long data sequences, reducing the need to segment time series into shorter intervals for model training and inference....
Foundation Models for Cross-Domain EEG Analysis Application: A Survey
Electroencephalography (EEG) analysis stands at the forefront of neuroscience and artificial intelligence research, where foundation models are reshaping the traditional EEG analysis paradigm by...
HEEGNet: Hyperbolic Embeddings for EEG
Electroencephalography (EEG)-based brain-computer interfaces facilitate direct communication with a computer, enabling promising applications in human-computer interactions. However, their utility...
BrainNet: Epileptic Wave Detection from SEEG with Hierarchical Graph Diffusion Learning
Epilepsy is one of the most serious neurological diseases, affecting 1-2% of the world's population. The diagnosis of epilepsy depends heavily on the recognition of epileptic waves, i.e.,...
Evaluation in EEG Emotion Recognition: State-of-the-Art Review and Unified Framework
Electroencephalography-based Emotion Recognition (EEG-ER) has become a growing research area in recent years. Analyzing 216 papers published between 2018 and 2023, we uncover that the field lacks...
MBrain: A Multi-channel Self-Supervised Learning Framework for Brain Signals
Brain signals are important quantitative data for understanding physiological activities and diseases of human brain. Most existing studies pay attention to supervised learning methods, which,...
Fine-Tuning Strategies for Continual Online EEG Motor Imagery Decoding: Insights from a Large-Scale Longitudinal Study
This study investigates continual fine-tuning strategies for deep learning in online longitudinal electroencephalography (EEG) motor imagery (MI) decoding within a causal setting involving a large...
Modeling Multivariate Biosignals With Graph Neural Networks and Structured State Space Models
Multivariate biosignals are prevalent in many medical domains, such as electroencephalography, polysomnography, and electrocardiography. Modeling spatiotemporal dependencies in multivariate...
EEG Artifact Detection and Correction with Deep Autoencoders
EEG signals convey important information about brain activity both in healthy and pathological conditions. However, they are inherently noisy, which poses significant challenges for accurate...
Cinematic Mindscapes: High-quality Video Reconstruction from Brain Activity
Reconstructing human vision from brain activities has been an appealing task that helps to understand our cognitive process. Even though recent research has seen great success in reconstructing...
Spatial-Functional awareness Transformer-based graph archetype contrastive learning for Decoding Visual Neural Representations from EEG
Decoding visual neural representations from Electroencephalography (EEG) signals remains a formidable challenge due to their high-dimensional, noisy, and non-Euclidean nature. In this work, we...
Multi-class Decoding of Attended Speaker Direction Using Electroencephalogram and Audio Spatial Spectrum
Decoding the directional focus of an attended speaker from listeners' electroencephalogram (EEG) signals is essential for developing brain-computer interfaces to improve the quality of life for...