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

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Showing 276–300 of 1655 papers

TitleStatusHype
End-to-end Sleep Staging with Raw Single Channel EEG using Deep Residual ConvNetsCode0
Enhanced Cross-Dataset Electroencephalogram-based Emotion Recognition using Unsupervised Domain AdaptationCode0
Embedding neurophysiological signalsCode0
AI-based artistic representation of emotions from EEG signals: a discussion on fairness, inclusion, and aestheticsCode0
EMMT: A simultaneous eye-tracking, 4-electrode EEG and audio corpus for multi-modal reading and translation scenariosCode0
Efficient Hierarchical Bayesian Inference for Spatio-temporal Regression Models in NeuroimagingCode0
EnK: Encoding time-information in convolutionCode0
Fast and Accurate Multiclass Inference for MI-BCIs Using Large Multiscale Temporal and Spectral FeaturesCode0
Imagined speech classification using EEGCode0
An Open-source Toolbox for Analysing and Processing PhysioNet Databases in MATLAB and OctaveCode0
EEGsig: an open-source machine learning-based toolbox for EEG signal processingCode0
EEGDiR: Electroencephalogram denoising network for temporal information storage and global modeling through Retentive NetworkCode0
EEG4Students: An Experimental Design for EEG Data Collection and Machine Learning AnalysisCode0
Dynamical Embedding of Single Channel Electroencephalogram for Artifact Subspace ReconstructionCode0
Boosting Factor-Specific Functional Historical Models for the Detection of Synchronisation in Bioelectrical SignalsCode0
Direct Estimation of Differential Functional Graphical ModelsCode0
Deep Riemannian Networks for End-to-End EEG DecodingCode0
Detection of REM Sleep Behaviour Disorder by Automated Polysomnography AnalysisCode0
Deep learning with convolutional neural networks for decoding and visualization of EEG pathologyCode0
Deep learning-based electroencephalography analysis: a systematic reviewCode0
Deep Learning Human Mind for Automated Visual ClassificationCode0
Deep Optimal Transport for Domain Adaptation on SPD ManifoldsCode0
DOSED: a deep learning approach to detect multiple sleep micro-events in EEG signalCode0
Decoding kinetic features of hand motor preparation from single‐trial EEG using convolutional neural networksCode0
Decoding P300 Variability using Convolutional Neural NetworksCode0
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All datasetsSEED-IVHS-SSVEPSEED

Benchmark Results

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