Electroencephalogram (EEG)
332 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 with no code
MindArm: Mechanized Intelligent Non-Invasive Neuro-Driven Prosthetic Arm System
This demonstrates that our MindArm provides a novel approach for an alternate low-cost mind-controlled prosthetic devices for all patients.
A simple EEG-based decision tool for neonatal therapeutic hypothermia in hypoxic-ischemic encephalopathy
Quantized probability densities of slow spectral features (delta power) from early conventional EEG (withing 6 hours of birth) revealed significant differences in slow spectral dynamics between infants with mild HIE grades and those relevant for hypothermia.
Synthesizing EEG Signals from Event-Related Potential Paradigms with Conditional Diffusion Models
Data scarcity in the brain-computer interface field can be alleviated through the use of generative models, specifically diffusion models.
Identifying Attention-Deficit/Hyperactivity Disorder through the electroencephalogram complexity
The distribution of values in the $(c, q)$-space derived from $q$-statistics seems to be a promising biomarker for ADHD diagnosis.
Reconstructing Visual Stimulus Images from EEG Signals Based on Deep Visual Representation Model
Considering the advantages of low cost and easy portability of the electroencephalogram (EEG) acquisition equipments, we propose a novel image reconstruction method based on EEG signals in this paper.
Physics-informed and Unsupervised Riemannian Domain Adaptation for Machine Learning on Heterogeneous EEG Datasets
Combining electroencephalogram (EEG) datasets for supervised machine learning (ML) is challenging due to session, subject, and device variability.
FAST functional connectivity implicates P300 connectivity in working memory deficits in Alzheimer's disease
The resulting average connectivity matrix, containing information on the strongest general connections for the tasks, is used as a filter to analyse the transient high temporal resolution functional connectivity of individual subjects.
EGNN-C+: Interpretable Evolving Granular Neural Network and Application in Classification of Weakly-Supervised EEG Data Streams
As an application, we focus on the classification of emotion-related patterns within electroencephalogram (EEG) signals.
EEG classifier cross-task transfer to avoid training sessions in robot-assisted rehabilitation
Furthermore, the classifier was evaluated to predict unilateral movements by only beeing trained on the data of the bilateral movement condition.
Contrastive Learning of Shared Spatiotemporal EEG Representations Across Individuals for Naturalistic Neuroscience
Targeting the Electroencephalogram (EEG) technique, known for its rich spatial and temporal information, this study presents a general framework for Contrastive Learning of Shared SpatioTemporal EEG Representations across individuals (CL-SSTER).
