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 401–425 of 1655 papers

TitleStatusHype
ActiveClean: Interactive Data Cleaning While Learning Convex Loss Models—0
A3E: Aligned and Augmented Adversarial Ensemble for Accurate, Robust and Privacy-Preserving EEG Decoding—0
Automated Detection of Patients in Hospital Video Recordings—0
Automated Detection of Abnormalities from an EEG Recording of Epilepsy Patients With a Compact Convolutional Neural Network—0
Analyzing EEG Data with Machine and Deep Learning: A Benchmark—0
Automated Classification of Sleep Stages and EEG Artifacts in Mice with Deep Learning—0
Automated Classification of Seizures against Nonseizures: A Deep Learning Approach—0
Analysis of Relation between Motor Activity and Imaginary EEG Records—0
Automated Classification of L/R Hand Movement EEG Signals using Advanced Feature Extraction and Machine Learning—0
Analysis of Microstate Organization During Emotional Events—0
A unifying Bayesian approach for preterm brain-age prediction that models EEG sleep transitions over age—0
A unified information-theoretic model of EEG signatures of human language processing—0
Analysis of Gait-Event-related Brain Potentials During Instructed And Spontaneous Treadmill Walking -- Technical Affordances and used Methods—0
A contrastive-learning approach for auditory attention detection—0
Auditory Attention Decoding from EEG using Convolutional Recurrent Neural Network—0
A Two-Stage Efficient 3-D CNN Framework for EEG Based Emotion Recognition—0
A tutorial on group effective connectivity analysis, part 2: second level analysis with PEB—0
Analysis of EEG data using complex geometric structurization—0
Adversarial Feature Learning in Brain Interfacing: An Experimental Study on Eliminating Drowsiness Effects—0
Attention Patterns Detection using Brain Computer Interfaces—0
Attention-based Transfer Learning for Brain-computer Interface—0
Analysis of Cardiovascular Changes Caused by Epileptic Seizures in Human Photoplethysmogram Signal—0
A TSK-type Convolutional Recurrent Fuzzy Network for Predicting Driving Fatigue—0
Analysis of Blink Rate Variability during reading and memory testing—0
Adversarial Deep Learning in EEG Biometrics—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