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 1151–1175 of 1655 papers

TitleStatusHype
Lightweight Convolution Transformer for Cross-patient Seizure Detection in Multi-channel EEG Signals—0
Linking Attention-Based Multiscale CNN With Dynamical GCN for Driving Fatigue Detection—0
Locally temporal-spatial pattern learning with graph attention mechanism for EEG-based emotion recognition—0
Local Model Feature Transformations—0
Locked in Syndrome Machine Learning Classification using Sentence Comprehension EEG Data—0
Long-term changes in functional connectivity predict responses to intracranial stimulation of the human brain—0
Low Latency Real-Time Seizure Detection Using Transfer Deep Learning—0
M2LADS Demo: A System for Generating Multimodal Learning Analytics Dashboards—0
Machine Learning Applications on Neuroimaging for Diagnosis and Prognosis of Epilepsy: A Review—0
Machine Learning Approaches for Detecting the Depression from Resting-State Electroencephalogram (EEG): A Review Study—0
Machine learning approaches in Detecting the Depression from Resting-state Electroencephalogram (EEG): A Review Study—0
Machine Learning-Based Detection of Parkinson's Disease From Resting-State EEG: A Multi-Center Study—0
Machine Learning-based EEG Applications and Markets—0
Machine Learning Fairness for Depression Detection using EEG Data—0
Machine Learning For Classification Of Antithetical Emotional States—0
Machine Learning for Motor Learning: EEG-based Continuous Assessment of Cognitive Engagement for Adaptive Rehabilitation Robots—0
Machine Learning for Predicting Epileptic Seizures Using EEG Signals: A Review—0
Machine Learning for removing EEG artifacts: Setting the benchmark—0
Toward cross-subject and cross-session generalization in EEG-based emotion recognition: Systematic review, taxonomy, and methods—0
Machine learning techniques for the Schizophrenia diagnosis: A comprehensive review and future research directions—0
Machine learning with electroencephalography features for precise diagnosis of depression subtypes—0
Machine learning without a feature set for detecting bursts in the EEG of preterm infants—0
MAEEG: Masked Auto-encoder for EEG Representation Learning—0
Maximum Covariance Unfolding : Manifold Learning for Bimodal Data—0
Measuring Consciousness—0
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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