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 1501–1525 of 1655 papers

TitleStatusHype
Bayesian multi--dipole localization and uncertainty quantification from simultaneous EEG and MEG recordings—0
Bayesian Nonparametric Models for Synchronous Brain-Computer Interfaces—0
Bayesian Topological Learning for Brain State Classification—0
BCI learning induces core-periphery reorganization in M/EEG multiplex brain networks—0
BCI-Walls: A robust methodology to predict success or failure in brain computer interfaces—0
Benchmarking Domain Generalization on EEG-based Emotion Recognition—0
Benchmarking real-time algorithms for in-phase auditory stimulation of low amplitude slow waves with wearable EEG devices during sleep—0
Benchmarking Uncertainty Quantification on Biosignal Classification Tasks under Dataset Shift—0
Benefit-aware Early Prediction of Health Outcomes on Multivariate EEG Time Series—0
Beware of Overestimated Decoding Performance Arising from Temporal Autocorrelations in Electroencephalogram Signals—0
BiCurNet: Pre-Movement EEG based Neural Decoder for Biceps Curl Trajectory Estimation—0
Bidirectional Inference Networks: A Class of Deep Bayesian Networks for Health Profiling—0
Binary and Multiclass Classifiers based on Multitaper Spectral Features for Epilepsy Detection—0
Binary classification of multi-channel EEG records based on the ε-complexity of continuous vector functions—0
Binary Single-dimensional Convolutional Neural Network for Seizure Prediction—0
Bioelectrical brain activity can predict prosocial behavior—0
Biomagnetic signals recorded during transcranial magnetic stimulation (TMS)-evoked peripheral muscular activity—0
Biophysical aspects of neurocognitive modeling with long-term sustained temperature variations—0
Bispectrum-based Cross-frequency Functional Connectivity: Classification of Alzheimer's disease—0
Blink Rate Variability during resting and reading sessions—0
Boosted Convolutional Neural Networks for Motor Imagery EEG Decoding with Multiwavelet-based Time-Frequency Conditional Granger Causality Analysis—0
Boosting Template-based SSVEP Decoding by Cross-domain Transfer Learning—0
BRAIN2DEPTH: Lightweight CNN Model for Classification of Cognitive States from EEG Recordings—0
Brain2Vec: A Deep Learning Framework for EEG-Based Stress Detection Using CNN-LSTM-Attention—0
Brain Age from the Electroencephalogram of Sleep—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