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
Mental Task Classification Using Electroencephalogram SignalCode0
Recursive Estimation of User Intent from Noninvasive Electroencephalography using Discriminative ModelsCode0
MERLiN: Mixture Effect Recovery in Linear NetworksCode0
Metrics for Multivariate DictionariesCode0
FBDNN: Filter Banks and Deep Neural Networks for Portable and Fast Brain-Computer InterfacesCode0
Concept-based explainability for an EEG transformer modelCode0
MICAL: Mutual Information-Based CNN-Aided Learned FactorCode0
DOSED: a deep learning approach to detect multiple sleep micro-events in EEG signalCode0
Granger Causality using Neural NetworksCode0
Graph Convolutional Neural Networks for analysis of EEG signals, BCI applicationCode0
Fast and Accurate Multiclass Inference for MI-BCIs Using Large Multiscale Temporal and Spectral FeaturesCode0
GraphSleepNet: Adaptive Spatial-Temporal Graph Convolutional Networks for Sleep Stage ClassificationCode0
Direct Estimation of Differential Functional Graphical ModelsCode0
On the effects of pseudorandom and quantum-random number generators in soft computingCode0
Characterising Alzheimer's Disease with EEG-based Energy Landscape AnalysisCode0
On the interpretation of linear Riemannian tangent space model parameters in M/EEGCode0
Short-length SSVEP data extension by a novel generative adversarial networks based frameworkCode0
Tensor Decomposition of Large-scale Clinical EEGs Reveals Interpretable Patterns of Brain PhysiologyCode0
On the use of Pairwise Distance Learning for Brain Signal Classification with Limited ObservationsCode0
Extreme Learning Machine design for dealing with unrepresentative featuresCode0
A Novel Use of Discrete Wavelet Transform Features in the Prediction of Epileptic Seizures from EEG DataCode0
HHHFL: Hierarchical Heterogeneous Horizontal Federated Learning for ElectroencephalographyCode0
Upper Limb Movement Recognition utilising EEG and EMG Signals for Rehabilitative RoboticsCode0
TRUST-LAPSE: An Explainable and Actionable Mistrust Scoring Framework for Model MonitoringCode0
Open and free EEG datasets for epilepsy diagnosisCode0
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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