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 1576–1600 of 1655 papers

TitleStatusHype
Exploiting Multiple EEG Data Domains with Adversarial LearningCode0
Explainable Data Poison Attacks on Human Emotion Evaluation Systems based on EEG SignalsCode0
The use of Multi-domain Electroencephalogram Representations in the building of Models based on Convolutional and Recurrent Neural Networks for Epilepsy DetectionCode0
Bayesian Inference on Brain-Computer Interfaces via GLASSCode0
Avoiding Post-Processing with Event-Based Detection in Biomedical SignalsCode0
Cogni-Net: Cognitive Feature Learning through Deep Visual PerceptionCode0
Intra- and Inter-epoch Temporal Context Network (IITNet) Using Sub-epoch Features for Automatic Sleep Scoring on Raw Single-channel EEGCode0
Intracerebral EEG Artifact Identification Using Convolutional Neural NetworksCode0
A library of quantitative markers of seizure severityCode0
Time Majority Voting, a PC-based EEG Classifier for Non-expert UsersCode0
Ensemble learning using individual neonatal data for seizure detectionCode0
Signal2Image Modules in Deep Neural Networks for EEG ClassificationCode0
Advancing NLP with Cognitive Language Processing SignalsCode0
Scope and Arbitration in Machine Learning Clinical EEG ClassificationCode0
Deep Feature Learning for EEG RecordingsCode0
CogBERT: Cognition-Guided Pre-trained Language ModelsCode0
Is the brain macroscopically linear? A system identification of resting state dynamicsCode0
Convolutional Monge Mapping Normalization for learning on sleep dataCode0
Automated Pipeline for EEG Artifact Reduction (APPEAR) Recorded during fMRICode0
Joint Learning of Full-structure Noise in Hierarchical Bayesian Regression ModelsCode0
Screening for REM Sleep Behaviour Disorder with Minimal SensorsCode0
KAM -- a Kernel Attention Module for Emotion Classification with EEG DataCode0
Enriching Large-Scale Eventuality Knowledge Graph with Entailment RelationsCode0
Automated Brain Disorders Diagnosis Through Deep Neural NetworksCode0
SCVCNet: Sliding cross-vector convolution network for cross-task and inter-individual-set EEG-based cognitive workload recognitionCode0
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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