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
Coupled Support Tensor Machine Classification for Multimodal Neuroimaging DataCode0
Converting Your Thoughts to Texts: Enabling Brain Typing via Deep Feature Learning of EEG SignalsCode0
Signal2Image Modules in Deep Neural Networks for EEG ClassificationCode0
Convolutional Monge Mapping Normalization for learning on sleep dataCode0
Alignment-Based Adversarial Training (ABAT) for Improving the Robustness and Accuracy of EEG-Based BCIsCode0
Classification of BCI-EEG based on augmented covariance matrixCode0
Cross-validation in high-dimensional spaces: a lifeline for least-squares models and multi-class LDACode0
Decoding Envelope and Frequency-Following EEG Responses to Continuous Speech Using Deep Neural NetworksCode0
Complex common spatial patterns on time-frequency decomposed EEG for brain-computer interfaceCode0
Comparative evaluation of state-of-the-art algorithms for SSVEP-based BCIsCode0
Applying advanced machine learning models to classify electro-physiological activity of human brain for use in biometric identificationCode0
Concept-based explainability for an EEG transformer modelCode0
CogniVal: A Framework for Cognitive Word Embedding EvaluationCode0
Multiple Time Series Fusion Based on LSTM An Application to CAP A Phase Classification Using EEGCode0
Classification of epileptic seizures in EEG data based on iterative gated graph convolution networkCode0
Compact Convolutional Neural Networks for Classification of Asynchronous Steady-state Visual Evoked PotentialsCode0
Conditional canonical correlation estimation based on covariates with random forestsCode0
CogAlign: Learning to Align Textual Neural Representations to Cognitive Language Processing SignalsCode0
CogBERT: Cognition-Guided Pre-trained Language ModelsCode0
RIGOLETTO -- RIemannian GeOmetry LEarning: applicaTion To cOnnectivity. A contribution to the Clinical BCI Challenge -- WCCI2020Code0
Active Learning for Regression Using Greedy SamplingCode0
Cogni-Net: Cognitive Feature Learning through Deep Visual PerceptionCode0
Classification of multivariate weakly-labelled time-series with attentionCode0
Node-wise Domain Adaptation Based on Transferable Attention for Recognizing Road Rage via EEGCode0
Context tree selection for functional dataCode0
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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