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 1476–1500 of 1655 papers

TitleStatusHype
Automated Human Mind Reading Using EEG Signals for Seizure Detection—0
Automatic Analysis of EEGs Using Big Data and Hybrid Deep Learning Architectures—0
Automatic detection of abnormal EEG signals using wavelet feature extraction and gradient boosting decision tree—0
Automatic Detection of Arousals during Sleep using Multiple Physiological Signals—0
Automatic Detection of Epileptiform Discharges in the EEG—0
Automatic Diagnosis of Schizophrenia in EEG Signals Using CNN-LSTM Models—0
Automatic Electrodes Detection during simultaneous EEG/fMRI acquisition—0
Automatic Emotion Recognition (AER) System based on Two-Level Ensemble of Lightweight Deep CNN Models—0
Automatic Identification of Epileptic Seizures from EEG Signals using Sparse Representation-based Classification—0
Automatic Micro-sleep Detection under Car-driving Simulation Environment using Night-sleep EEG—0
Automatic Muscle Artifacts Identification and Removal from Single-Channel EEG Using Wavelet Transform with Meta-heuristically Optimized Non-local Means Filter—0
Automatic Seizure Prediction using CNN and LSTM—0
Automatic sleep monitoring using ear-EEG—0
Automatic Sleep Stage Scoring with Single-Channel EEG Using Convolutional Neural Networks—0
Automatic Sleep Staging of EEG Signals: Recent Development, Challenges, and Future Directions—0
AutoTransfer: Subject Transfer Learning with Censored Representations on Biosignals Data—0
Neural criticality from effective latent variables—0
A Wearable EEG System for Closed-Loop Neuromodulation of High-Frequency Sleep-Related Oscillations—0
Backdoor Attacks against Transfer Learning with Pre-trained Deep Learning Models—0
Backward Renormalization Priors and the Cortical Source Localization Problem with EEG or MEG—0
Ballistocardiogram artifact reduction in simultaneous EEG-fMRI using deep learning—0
Bandwidth-efficient distributed neural network architectures with application to body sensor networks—0
Bayesian Belief Updating of Spatiotemporal Seizure Dynamics—0
Bayesian deep neural networks for low-cost neurophysiological markers of Alzheimer's disease severity—0
Bayesian fusion and multimodal DCM for EEG and fMRI—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