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 351–375 of 1655 papers

TitleStatusHype
A CNN-Transformer Deep Learning Model for Real-time Sleep Stage Classification in an Energy-Constrained Wireless Device—0
Causal interpretation rules for encoding and decoding models in neuroimaging—0
Classification of ADHD and Healthy Children Using EEG Based Multi-Band Spatial Features Enhancement—0
Classification of auditory stimuli from EEG signals with a regulated recurrent neural network reservoir—0
Classification of Autism Spectrum Disorder Using Supervised Learning of Brain Connectivity Measures Extracted from Synchrostates—0
Application of statistical analysis to working memory problem—0
Classification of Brainwave Signals Based on Hybrid Deep Learning and an Evolutionary Algorithm—0
Classification of Distraction Levels Using Hybrid Deep Neural Networks From EEG Signals—0
A Novel TSK Fuzzy System Incorporating Multi-view Collaborative Transfer Learning for Personalized Epileptic EEG Detection—0
Classification of EEG Motor Imagery Using Deep Learning for Brain-Computer Interface Systems—0
Classification of EEG Signal based on non-Gaussian Neutral Vector—0
Classification of EEG Signals using Genetic Programming for Feature Construction—0
Classification of Electroencephalograms during Mathematical Calculations Using Deep Learning—0
Classification of Epileptic EEG Signals by Wavelet based CFC—0
Approximating DTW with a convolutional neural network on EEG data—0
Classification of eye-state using EEG recordings: speed-up gains using signal epochs and mutual information measure—0
A Decomposition-Based Hybrid Ensemble CNN Framework for Driver Fatigue Recognition—0
Classifying sleep-wake stages through recurrent neural networks using pulse oximetry signals—0
Carbogen inhalation during Non-Convulsive Status Epilepticus: A quantitative analysis of EEG recordings—0
Classification of Hand Movements from EEG using a Deep Attention-based LSTM Network—0
Classification of Imagined Speech Using Siamese Neural Network—0
Are Brain-Computer Interfaces Feasible with Integrated Photonic Chips?—0
A Recurrent Probabilistic Neural Network with Dimensionality Reduction Based on Time-series Discriminant Component Analysis—0
Classification of Perceived Human Stress using Physiological Signals—0
Capsule Attention for Multimodal EEG-EOG Representation Learning with Application to Driver Vigilance Estimation—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