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
Deep learning with convolutional neural networks for decoding and visualization of EEG pathologyCode0
Multichannel sleep spindle detection using sparse low-rank optimizationCode0
Brain Responses During Robot-Error Observation—0
Applying advanced machine learning models to classify electro-physiological activity of human brain for use in biometric identificationCode0
SPLICE: Fully Tractable Hierarchical Extension of ICA with Pooling—0
SLEEPNET: Automated Sleep Staging System via Deep Learning—0
A comparison of single-trial EEG classification and EEG-informed fMRI across three MR compatible EEG recording systems—0
Application of Dictionary Learning in Alleviating Computational Burden of EEG Source Localization—0
Stance leg and surface stability modulate cortical activity during human single leg stance—0
Multiscale Granger causality analysis by à trous wavelet transformCode0
A Generalised Seizure Prediction with Convolutional Neural Networks for Intracranial and Scalp Electroencephalogram Data Analysis—0
Learning Cognitive Features from Gaze Data for Sentiment and Sarcasm Classification using Convolutional Neural Network—0
Sparsity Enables Estimation of both Subcortical and Cortical Activity from MEG and EEG—0
Compressed Factorization: Fast and Accurate Low-Rank Factorization of Compressively-Sensed Data—0
Individual Recognition in Schizophrenia using Deep Learning Methods with Random Forest and Voting Classifiers: Insights from Resting State EEG Streams—0
Bayesian multi--dipole localization and uncertainty quantification from simultaneous EEG and MEG recordings—0
The interplay between long- and short-range temporal correlations shapes cortex dynamics across vigilance states—0
Decline of long-range temporal correlations in the human brain during sustained wakefulness—0
Deep Recurrent Neural Networks for seizure detection and early seizure detection systems—0
DeepKey: An EEG and Gait Based Dual-Authentication System—0
Generalized Concomitant Multi-Task Lasso for sparse multimodal regressionCode0
Bayesian Belief Updating of Spatiotemporal Seizure Dynamics—0
Improving classification accuracy of feedforward neural networks for spiking neuromorphic chips—0
Deep neural networks on graph signals for brain imaging analysis—0
Spatial-Temporal Recurrent Neural Network for 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