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 501–525 of 1655 papers

TitleStatusHype
A Novel Method for Epileptic Seizure Detection Using Coupled Hidden Markov Models—0
Deep Classification of Epileptic Signals—0
Brain-Supervised Image Editing—0
Automatic Diagnosis of Schizophrenia in EEG Signals Using CNN-LSTM Models—0
Deep Convolutional Neural Network for Automated Detection of Mind Wandering using EEG Signals—0
Deep Convolutional Neural Networks for Interpretable Analysis of EEG Sleep Stage Scoring—0
Deep convolutional recurrent neural network for short-interval EEG motor imagery classification—0
Deep Correlation Analysis for Audio-EEG Decoding—0
Automatic Identification of Epileptic Seizures from EEG Signals using Sparse Representation-based Classification—0
Deep Feature Mining via Attention-based BiLSTM-GCN for Human Motor Imagery Recognition—0
Brain Source Localization by Alternating Projection—0
A Novel Independent RNN Approach to Classification of Seizures against Non-seizures—0
Deep Neural Networks on EEG Signals to Predict Auditory Attention Score Using Gramian Angular Difference Field—0
Deep Recurrent Neural Networks for seizure detection and early seizure detection systems—0
Deep Transfer Learning for EEG-based Brain Computer Interface—0
Designing Pre-training Datasets from Unlabeled Data for EEG Classification with Transformers—0
Brain Signals to Rescue Aphasia, Apraxia and Dysarthria Speech Recognition—0
Localization of MEG and EEG Brain Signals by Alternating Projection—0
A Novel Deep Learning Architecture for Decoding Imagined Speech from EEG—0
Brain Signals Analysis Based Deep Learning Methods: Recent advances in the study of non-invasive brain signals—0
A novel convolutional neural network model to remove muscle artifacts from EEG—0
A Hybrid Deep Spatio-Temporal Attention-Based Model for Parkinson's Disease Diagnosis Using Resting State EEG Signals—0
Brain-scale Theta Band Functional Connectivity As A Signature of Slow Breathing and Breath-hold Phases—0
Brains and pseudorandom generators—0
A Novel Bi-hemispheric Discrepancy Model for EEG Emotion Recognition—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