SOTAVerified

Electroencephalogram (EEG)

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.

Papers

Showing 11261150 of 1655 papers

TitleStatusHype
SPLICE: Fully Tractable Hierarchical Extension of ICA with Pooling0
Spoken Speech Enhancement using EEG0
SSGCNet: A Sparse Spectra Graph Convolutional Network for Epileptic EEG Signal Classification0
SSRepL-ADHD: Adaptive Complex Representation Learning Framework for ADHD Detection from Visual Attention Tasks0
Stabilizing Subject Transfer in EEG Classification with Divergence Estimation0
Staging Epileptogenesis with Deep Neural Networks0
Stance leg and surface stability modulate cortical activity during human single leg stance0
Stationarity of Time-Series on Graph via Bivariate Translation Invariance0
Stationary and Sparse Denoising Approach for Corticomuscular Causality Estimation0
StationPlot: A New Non-stationarity Quantification Tool for Detection of Epileptic Seizures0
Statistical control for spatio-temporal MEG/EEG source imaging with desparsified mutli-task Lasso0
STEAM-EEG: Spatiotemporal EEG Analysis with Markov Transfer Fields and Attentive CNNs0
STILN: A Novel Spatial-Temporal Information Learning Network for EEG-based Emotion Recognition0
StrokeSight: A Novel EEG-Based Diagnostic System for Strokes Using Spectral Analysis and Deep Learning0
Structure-Preserving Graph Kernel for Brain Network Classification0
Studying Drowsiness Detection Performance while Driving through Scalable Machine Learning Models using Electroencephalography0
Study of cognitive component of auditory attention to natural speech events0
Study on Compressed Sensing of Action Potential0
Sub-100uW Multispectral Riemannian Classification for EEG-based Brain--Machine Interfaces0
Subject-independent trajectory prediction using pre-movement EEG during grasp and lift task0
Subject-Independent Brain-Computer Interface for Decoding High-Level Visual Imagery Tasks0
Subject-Independent Brain-Computer Interfaces with Open-Set Subject Recognition0
Subject-Independent Deep Architecture for EEG-based Motor Imagery Classification0
Subject independent EEG-based BCI decoding0
Subject Independent Emotion Recognition using EEG Signals Employing Attention Driven Neural Networks0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1BiHDMAccuracy74.35Unverified
2DGCNNAccuracy69.88Unverified
3DBNAccuracy66.77Unverified
#ModelMetricClaimedVerifiedStatus
1MultitaskSSVEPAccuracy (5-fold)92.2Unverified
#ModelMetricClaimedVerifiedStatus
1DBNAccuracy86.08Unverified