Electroencephalogram (EEG)
334 papers with code • 3 benchmarks • 7 datasets
Electroencephalogram (EEG) is a method of recording brain activity using electrophysiological indexes. When the brain is active, a large number of postsynaptic potentials generated synchronously by neurons are formed after summation. It records the changes of electric waves during brain activity and is the overall reflection of the electrophysiological activities of brain nerve cells on the surface of cerebral cortex or scalp. Brain waves originate from the postsynaptic potential of the apical dendrites of pyramidal cells. The formation of synchronous rhythm of EEG is also related to the activity of nonspecific projection system of cortex and thalamus. EEG is the basic theoretical research of brain science. EEG monitoring is widely used in its clinical application.
Libraries
Use these libraries to find Electroencephalogram (EEG) models and implementations
SEED
ICLabel
CWL EEG/fMRI Dataset
Subtasks
Latest papers
State-transition dynamics of resting-state functional magnetic resonance imaging data: Model comparison and test-to-retest analysis
Electroencephalogram (EEG) microstate analysis entails finding dynamics of quasi-stable and generally recurrent discrete states in multichannel EEG time series data and relating properties of the estimated state-transition dynamics to observables such as cognition and behavior.
ViT2EEG: Leveraging Hybrid Pretrained Vision Transformers for EEG Data
ruiqirichard/eegeyenet-vit
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In this study, we demonstrate the application of a hybrid Vision Transformer (ViT) model, pretrained on ImageNet, on an electroencephalogram (EEG) regression task.
Concept-based explainability for an EEG transformer model
Deep learning models are complex due to their size, structure, and inherent randomness in training procedures.
DreamDiffusion: Generating High-Quality Images from Brain EEG Signals
This paper introduces DreamDiffusion, a novel method for generating high-quality images directly from brain electroencephalogram (EEG) signals, without the need to translate thoughts into text.
Convolutional Monge Mapping Normalization for learning on sleep data
pythonot/convolutional-monge-mapping-normalization
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In many machine learning applications on signals and biomedical data, especially electroencephalogram (EEG), one major challenge is the variability of the data across subjects, sessions, and hardware devices.
Bayesian Inference on Brain-Computer Interfaces via GLASS
Brain-computer interfaces (BCIs), particularly the P300 BCI, facilitate direct communication between the brain and computers.
Multimodal Brain-Computer Interface for In-Vehicle Driver Cognitive Load Measurement: Dataset and Baselines
Through this paper, we introduce a novel driver cognitive load assessment dataset, CL-Drive, which contains Electroencephalogram (EEG) signals along with other physiological signals such as Electrocardiography (ECG) and Electrodermal Activity (EDA) as well as eye tracking data.
Are uGLAD? Time will tell!
harshs27/tglad
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Segmentation of multivariate time series data is a technique for identifying meaningful patterns or changes in the time series that can signal a shift in the system's behavior.
Towards Domain Generalization for ECG and EEG Classification: Algorithms and Benchmarks
Our objective in this work is to propose a benchmark for evaluating DG algorithms, in addition to introducing a novel architecture for tackling DG in biosignal classification.
Challenges facing the explainability of age prediction models: case study for two modalities
The prediction of age is a challenging task with various practical applications in high-impact fields like the healthcare domain or criminology.
