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
Emotion Recognition With Temporarily Localized 'Emotional Events' in Naturalistic Context—0
Graph Neural Networks on SPD Manifolds for Motor Imagery Classification: A Perspective from the Time-Frequency AnalysisCode1
Towards emotion recognition for virtual environments: an evaluation of EEG features on benchmark dataset—0
Non-Contrastive Learning-based Behavioural Biometrics for Smart IoT Devices—0
fMRI from EEG is only Deep Learning away: the use of interpretable DL to unravel EEG-fMRI relationshipsCode1
Sparse Dynamical Features generation, application to Parkinson's Disease diagnosis—0
Analysis of Microstate Organization During Emotional Events—0
A Framework to Evaluate Independent Component Analysis applied to EEG signal: testing on the Picard algorithmCode0
Extreme-Long-short Term Memory for Time-series Prediction—0
Bandwidth-efficient distributed neural network architectures with application to body sensor networks—0
Toward the application of XAI methods in EEG-based systems—0
CLEEGN: A Convolutional Neural Network for Plug-and-Play Automatic EEG Reconstruction—0
Inner speech recognition through electroencephalographic signals—0
The evolution of AI approaches for motor imagery EEG-based BCIs—0
Self-supervised Learning for Label-Efficient Sleep Stage Classification: A Comprehensive EvaluationCode1
Modeling and Mining Multi-Aspect Graphs With Scalable Streaming Tensor Decomposition—0
A Transformer-based deep neural network model for SSVEP classificationCode1
A review on Epileptic Seizure Detection using Machine Learning—0
MAtt: A Manifold Attention Network for EEG DecodingCode1
On The Effects Of Data Normalisation For Domain Adaptation On EEG Data—0
Cross Task Neural Architecture Search for EEG Signal ClassificationsCode1
CogBERT: Cognition-Guided Pre-trained Language ModelsCode0
quEEGNet: Quantum AI for Biosignal Processing—0
EEG-based Image Feature Extraction for Visual Classification using Deep Learning—0
Community Detection in Multi-frequency EEG Networks—0
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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