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 801–825 of 1655 papers

TitleStatusHype
Feature selection for gesture recognition in Internet-of-Things for healthcare—0
Feature Selection via Dynamic Graph-based Attention Block in MI-based EEG Signals—0
EEG-based Image Feature Extraction for Visual Classification using Deep Learning—0
Federated deep transfer learning for EEG decoding using multiple BCI tasks—0
Brain Signals Analysis Based Deep Learning Methods: Recent advances in the study of non-invasive brain signals—0
Few-Shot Relation Learning with Attention for EEG-based Motor Imagery Classification—0
Few-shot time series segmentation using prototype-defined infinite hidden Markov models—0
Finding neural signatures for obesity through feature selection on source-localized EEG—0
EEG-Based Epileptic Seizure Prediction Using Temporal Multi-Channel Transformers—0
Multi-center validation study of automated classification of pathological slowing in adult scalp electroencephalograms via frequency features—0
FKreg: A MATLAB toolbox for fast Multivariate Kernel Regression—0
Fluctuations in EEG band power at subject-specific timescales over minutes to days explain changes in seizure evolutions—0
EEG Based Emotion Sensing using convolutional neural networks—0
A novel convolutional neural network model to remove muscle artifacts from EEG—0
A Hybrid Deep Spatio-Temporal Attention-Based Model for Parkinson's Disease Diagnosis Using Resting State EEG Signals—0
Frequency Recognition in SSVEP-based BCI using Multiset Canonical Correlation Analysis—0
From Epilepsy Seizures Classification to Detection: A Deep Learning-based Approach for Raw EEG Signals—0
EEG-based Emotion Recognition via Efficient Convolutional Neural Network and Contrastive Learning—0
Brain-scale Theta Band Functional Connectivity As A Signature of Slow Breathing and Breath-hold Phases—0
Functional Connectivity Methods for EEG-based Biometrics on a Large, Heterogeneous Dataset—0
Functional Graph Contrastive Learning of Hyperscanning EEG Reveals Emotional Contagion Evoked by Stereotype-Based Stressors—0
Functional Neural Networks: Shift invariant models for functional data with applications to EEG classification—0
Functional switching among dynamic neuronal hub-nodes in the brain induces transition of cognitive states—0
Fusing Pretrained ViTs with TCNet for Enhanced EEG Regression—0
EEG based Emotion Recognition of Image Stimuli—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