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 1626–1650 of 1655 papers

TitleStatusHype
Towards a Better Understanding Human Reading Comprehension with Brain SignalsCode0
Too Fine or Too Coarse? The Goldilocks Composition of Data Complexity for Robust Left-Right Eye-Tracking ClassifiersCode0
Random Convolution Kernels with Multi-Scale Decomposition for Preterm EEG Inter-burst DetectionCode0
Node-wise Domain Adaptation Based on Transferable Attention for Recognizing Road Rage via EEGCode0
Localization of Brain Activity from EEG/MEG Using MV-PURE FrameworkCode0
Noise-based cyberattacks generating fake P300 waves in brain–computer interfacesCode0
Self-Organization Towards 1/f Noise in Deep Neural NetworksCode0
Converting Your Thoughts to Texts: Enabling Brain Typing via Deep Feature Learning of EEG SignalsCode0
Classification of multivariate weakly-labelled time-series with attentionCode0
EMMT: A simultaneous eye-tracking, 4-electrode EEG and audio corpus for multi-modal reading and translation scenariosCode0
Reading Task Classification Using EEG and Eye-Tracking DataCode0
Embedding neurophysiological signalsCode0
Classification of Motor Imagery EEG Signals by Using a Divergence Based Convolutional Neural NetworkCode0
Real-time EEG-based Emotion Recognition Model using Principal Component Analysis and Tree-based Models for NeurohumanitiesCode0
Nonparametric Modeling of Dynamic Functional Connectivity in fMRI DataCode0
Classification of High-Dimensional Motor Imagery Tasks based on An End-to-end role assigned convolutional neural networkCode0
Mutual Information-driven Subject-invariant and Class-relevant Deep Representation Learning in BCICode0
Efficient Hierarchical Bayesian Inference for Spatio-temporal Regression Models in NeuroimagingCode0
Context tree selection for functional dataCode0
Classification of epileptic seizures in EEG data based on iterative gated graph convolution networkCode0
Using Explainable AI for EEG-based Reduced Montage Neonatal Seizure DetectionCode0
Classification of BCI-EEG based on augmented covariance matrixCode0
Conditional canonical correlation estimation based on covariates with random forestsCode0
Real-Time Sleep Staging using Deep Learning on a Smartphone for a Wearable EEGCode0
Seizure Type Classification using EEG signals and Machine Learning: Setting a benchmarkCode0
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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