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 376–400 of 1655 papers

TitleStatusHype
Deep learning-based electroencephalography analysis: a systematic reviewCode0
Hybrid multi-objective evolutionary algorithm based on Search Manager framework for big data optimization problemsCode0
Recursive Estimation of User Intent from Noninvasive Electroencephalography using Discriminative ModelsCode0
Automatic Identification of Epileptic Seizures from EEG Signals using Sparse Representation-based Classification—0
Automatic Emotion Recognition (AER) System based on Two-Level Ensemble of Lightweight Deep CNN Models—0
An EEG-based Image Annotation System—0
Automatic Electrodes Detection during simultaneous EEG/fMRI acquisition—0
Automatic Diagnosis of Schizophrenia in EEG Signals Using CNN-LSTM Models—0
An EEG-based approach for Parkinson's disease diagnosis using Capsule network—0
Affective Brain-Computer Interfaces: A Tutorial to Choose Performance Measuring Metric—0
Automatic Detection of Epileptiform Discharges in the EEG—0
Automatic Detection of Arousals during Sleep using Multiple Physiological Signals—0
Automatic detection of abnormal EEG signals using wavelet feature extraction and gradient boosting decision tree—0
An automated approach for task evaluation using EEG signals—0
Active Learning for Black-Box Adversarial Attacks in EEG-Based Brain-Computer Interfaces—0
Automatic Analysis of EEGs Using Big Data and Hybrid Deep Learning Architectures—0
An Artificial Intelligence Browser Architecture (AIBA) For Our Kind and Others: A Voice Name System Speech implementation with two warrants, Wake Neutrality and Value Preservation of Personally Identifiable Information—0
A Dynamic Domain Adaptation Deep Learning Network for EEG-based Motor Imagery Classification—0
Automated Human Mind Reading Using EEG Signals for Seizure Detection—0
Automated Feature Extraction on AsMap for Emotion Classification using EEG—0
An Analysis of Two Common Reference Points for EEGs—0
Automated EEG-based Screening of Depression Using Deep Convolutional Neural Network—0
Automated Diagnosis of Epilepsy Employing Multifractal Detrended Fluctuation Analysis Based Features—0
An amplitudes-perturbation data augmentation method in convolutional neural networks for EEG decoding—0
Adversarial Stimuli: Attacking Brain-Computer Interfaces via Perturbed Sensory Events—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