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 651–675 of 1655 papers

TitleStatusHype
EEG Cortical Source Feature based Hand Kinematics Decoding using Residual CNN-LSTM Neural Network—0
Detection of Sleep Oxygen Desaturations from Electroencephalogram Signals—0
Benefit-aware Early Prediction of Health Outcomes on Multivariate EEG Time Series—0
Calibration of off-the-shelf low-cost wearable EEG headset for application in field studies—0
A Neural-Inspired Architecture for EEG-Based Auditory Attention Detection—0
EEG emotion recognition using dynamical graph convolutional neural networks—0
A Generative Model to Synthesize EEG Data for Epileptic Seizure Prediction—0
EEG-Fest: Few-shot based Attention Network for Driver's Vigilance Estimation with EEG Signals—0
EEG fingerprinting: subject specific signature based on the aperiodic component of power spectrum—0
EEG Foundation Challenge: From Cross-Task to Cross-Subject EEG Decoding—0
EEG functional connectivity and deep learning for automatic diagnosis of brain disorders: Alzheimer's disease and schizophrenia—0
EEGFuseNet: Hybrid Unsupervised Deep Feature Characterization and Fusion for High-Dimensional EEG with An Application to Emotion Recognition—0
A Change Dynamic Model for the Online Detection of Gradual Change—0
Detection of Epileptic Seizures on EEG Signals Using ANFIS Classifier, Autoencoders and Fuzzy Entropies—0
EEG-GMACN: Interpretable EEG Graph Mutual Attention Convolutional Network—0
EEG-GNN: Graph Neural Networks for Classification of Electroencephalogram (EEG) Signals—0
Detection of epileptic seizure in EEG signals using linear least squares preprocessing—0
Novel Epileptic Seizure Detection Techniques and their Empirical Analysis—0
EEG-informed attended speaker extraction from recorded speech mixtures with application in neuro-steered hearing prostheses—0
EEG in the classroom: Synchronised neural recordings during video presentation—0
Detecting single-trial EEG evoked potential using a wavelet domain linear mixed model: application to error potentials classification—0
Benchmarking Uncertainty Quantification on Biosignal Classification Tasks under Dataset Shift—0
EEG Machine Learning for Analysis of Mild Traumatic Brain Injury: A survey—0
EEG machine learning with Higuchi fractal dimension and Sample Entropy as features for successful detection of depression—0
An Ensemble Learning Based Classification of Individual Finger Movement from 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