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 976–1000 of 1655 papers

TitleStatusHype
Classifying sleep-wake stages through recurrent neural networks using pulse oximetry signals—0
Review of Machine Learning Algorithms for Brain Stroke Diagnosis and Prognosis by EEG Analysis—0
Uncovering the structure of clinical EEG signals with self-supervised learningCode1
Evidence of Task-Independent Person-Specific Signatures in EEG using Subspace Techniques—0
Preliminary Assessment of hands motor imagery in theta- and beta-bands for Brain-Machine-Interfaces using functional connectivity analysis—0
Selection of Proper EEG Channels for Subject Intention Classification Using Deep Learning—0
Epileptic Seizure Prediction: A Semi-Dilated Convolutional Neural Network Architecture—0
Understanding Consumer Preferences for Movie Trailers from EEG using Machine Learning—0
Improving P300 Speller performance by means of optimization and machine learning—0
On the variability of functional connectivity and network measures in source-reconstructed EEG time-series—0
GraphSleepNet: Adaptive Spatial-Temporal Graph Convolutional Networks for Sleep Stage ClassificationCode0
Electromyogram (EMG) Removal by Adding Sources of EMG (ERASE) -- A novel ICA-based algorithm for removing myoelectric artifacts from EEG -- Part 2—0
Electromyogram (EMG) Removal by Adding Sources of EMG (ERASE) -- A novel ICA-based algorithm for removing myoelectric artifacts from EEG -- Part 1—0
Tensor Convolutional Sparse Coding with Low-Rank activations, an application to EEG analysisCode1
Transfer Learning for Motor Imagery Based Brain-Computer Interfaces: A Complete PipelineCode1
Non-Gaussianity Detection of EEG Signals Based on a Multivariate Scale Mixture Model for Diagnosis of Epileptic Seizures—0
Epileptic Seizures Detection Using Deep Learning Techniques: A Review—0
A Novel RL-assisted Deep Learning Framework for Task-informative Signals Selection and Classification for Spontaneous BCIs—0
Computing extracellular electric potentials from neuronal simulations—0
Subject-Aware Contrastive Learning for BiosignalsCode1
Accelerating Reinforcement Learning Agent with EEG-based Implicit Human Feedback—0
Emotion self-regulation training in major depressive disorder using simultaneous real-time fMRI and EEG neurofeedback—0
Application of statistical analysis to working memory problem—0
An Evoked Potential-Guided Deep Learning Brain Representation For Visual Classification—0
Attention-based Graph ResNet for Motor Intent Detection from Raw EEG signalsCode2
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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