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 1451–1475 of 1655 papers

TitleStatusHype
A statistical method for analyzing and comparing spatiotemporal cortical activation patterns—0
A streamable large-scale clinical EEG dataset for Deep Learning—0
A study of resting-state EEG biomarkers for depression recognition—0
A Survey and Tutorial of EEG-Based Brain Monitoring for Driver State Analysis—0
A Tale of Single-channel Electroencephalogram: Devices, Datasets, Signal Processing, Applications, and Future Directions—0
A Technique Based on Chaos for Brain Computer Interfacing—0
Interpretable Classification of Early Stage Parkinson's Disease from EEG—0
A Time-Series Scale Mixture Model of EEG with a Hidden Markov Structure for Epileptic Seizure Detection—0
A topological classifier to characterize brain states: When shape matters more than variance—0
A TSK-type Convolutional Recurrent Fuzzy Network for Predicting Driving Fatigue—0
Attention-based Transfer Learning for Brain-computer Interface—0
Attention Patterns Detection using Brain Computer Interfaces—0
A tutorial on group effective connectivity analysis, part 2: second level analysis with PEB—0
A Two-Stage Efficient 3-D CNN Framework for EEG Based Emotion Recognition—0
Auditory Attention Decoding from EEG using Convolutional Recurrent Neural Network—0
A unified information-theoretic model of EEG signatures of human language processing—0
A unifying Bayesian approach for preterm brain-age prediction that models EEG sleep transitions over age—0
Automated Classification of L/R Hand Movement EEG Signals using Advanced Feature Extraction and Machine Learning—0
Automated Classification of Seizures against Nonseizures: A Deep Learning Approach—0
Automated Classification of Sleep Stages and EEG Artifacts in Mice with Deep Learning—0
Automated Detection of Abnormalities from an EEG Recording of Epilepsy Patients With a Compact Convolutional Neural Network—0
Automated Detection of Patients in Hospital Video Recordings—0
Automated Diagnosis of Epilepsy Employing Multifractal Detrended Fluctuation Analysis Based Features—0
Automated EEG-based Screening of Depression Using Deep Convolutional Neural Network—0
Automated Feature Extraction on AsMap for Emotion Classification using EEG—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