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 1376–1400 of 1655 papers

TitleStatusHype
Brain-Computer Interface with Corrupted EEG Data: A Tensor Completion Approach—0
Early Seizure Detection with an Energy-Efficient Convolutional Neural Network on an Implantable Microcontroller—0
Investigating the Impact of CNN Depth on Neonatal Seizure Detection Performance—0
Neonatal EEG Interpretation and Decision Support Framework for Mobile Platforms—0
EEG-GAN: Generative adversarial networks for electroencephalograhic (EEG) brain signalsCode1
Robustifying Independent Component Analysis by Adjusting for Group-Wise Stationary NoiseCode0
A Hierarchical Fuzzy System for an Advanced Driving Assistance System—0
A Multi-Context Character Prediction Model for a Brain-Computer Interface—0
Deep Semantic Architecture with discriminative feature visualization for neuroimage analysis—0
Focal onset seizure prediction using convolutional networks—0
Adaptive neural network classifier for decoding MEG signals—0
Multivariate Convolutional Sparse Coding for Electromagnetic Brain SignalsCode0
Multitaper Spectral Estimation HDP-HMMs for EEG Sleep Inference—0
Brain Age from the Electroencephalogram of Sleep—0
Lehmer Transform and its Theoretical Properties—0
A New Method for Epileptic Seizure Classification in EEG Using Adapted Wavelet Packets—0
Offline EEG-Based Driver Drowsiness Estimation Using Enhanced Batch-Mode Active Learning (EBMAL) for Regression—0
Active Semi-supervised Transfer Learning (ASTL) for Offline BCI Calibration—0
WU-NEAT: A clinically validated, open- source MATLAB toolbox for limited-channel neonatal EEG analysis—0
Intracranial Error Detection via Deep Learning—0
Classification of Epileptic EEG Signals by Wavelet based CFC—0
Covariate Shift Estimation based Adaptive Ensemble Learning for Handling Non-Stationarity in Motor Imagery related EEG-based Brain-Computer Interface—0
Classification of auditory stimuli from EEG signals with a regulated recurrent neural network reservoir—0
A machine learning model for identifying cyclic alternating patterns in the sleeping brain—0
Automated EEG-based Screening of Depression Using Deep Convolutional Neural Network—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