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 251–275 of 1655 papers

TitleStatusHype
Ensemble learning using individual neonatal data for seizure detectionCode0
On the challenges of detecting MCI using EEG in the wildCode0
On the interpretation of linear Riemannian tangent space model parameters in M/EEGCode0
On the use of Pairwise Distance Learning for Brain Signal Classification with Limited ObservationsCode0
Enriching Large-Scale Eventuality Knowledge Graph with Entailment RelationsCode0
EnK: Encoding time-information in convolutionCode0
Avoiding Post-Processing with Event-Based Detection in Biomedical SignalsCode0
Enhanced Cross-Dataset Electroencephalogram-based Emotion Recognition using Unsupervised Domain AdaptationCode0
End-to-end Sleep Staging with Raw Single Channel EEG using Deep Residual ConvNetsCode0
Refining ADHD diagnosis with EEG: The impact of preprocessing and temporal segmentation on classification accuracyCode0
Applying advanced machine learning models to classify electro-physiological activity of human brain for use in biometric identificationCode0
Enhancing Affective Representations of Music-Induced EEG through Multimodal Supervision and latent Domain AdaptationCode0
Alignment-Based Adversarial Training (ABAT) for Improving the Robustness and Accuracy of EEG-Based BCIsCode0
Embedding neurophysiological signalsCode0
Efficient Hierarchical Bayesian Inference for Spatio-temporal Regression Models in NeuroimagingCode0
EMMT: A simultaneous eye-tracking, 4-electrode EEG and audio corpus for multi-modal reading and translation scenariosCode0
A library of quantitative markers of seizure severityCode0
A personalized and evolutionary algorithm for interpretable EEG epilepsy seizure predictionCode0
EEGsig: an open-source machine learning-based toolbox for EEG signal processingCode0
EEGDiR: Electroencephalogram denoising network for temporal information storage and global modeling through Retentive NetworkCode0
A Novel Use of Discrete Wavelet Transform Features in the Prediction of Epileptic Seizures from EEG DataCode0
AI-based artistic representation of emotions from EEG signals: a discussion on fairness, inclusion, and aestheticsCode0
Dynamical Embedding of Single Channel Electroencephalogram for Artifact Subspace ReconstructionCode0
EEG4Students: An Experimental Design for EEG Data Collection and Machine Learning AnalysisCode0
Extreme Learning Machine design for dealing with unrepresentative featuresCode0
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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