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 76–100 of 1655 papers

TitleStatusHype
A Deep Learning Approach for the Segmentation of Electroencephalography Data in Eye Tracking ApplicationsCode1
Motiflets -- Simple and Accurate Detection of Motifs in Time SeriesCode1
Investigating Brain Connectivity with Graph Neural Networks and GNNExplainerCode1
Learning Generative Factors of EEG Data with Variational auto-encodersCode1
SPD domain-specific batch normalization to crack interpretable unsupervised domain adaptation in EEGCode1
Neuro-BERT: Rethinking Masked Autoencoding for Self-supervised Neurological PretrainingCode1
EEG-ITNet: An Explainable Inception Temporal Convolutional Network for Motor Imagery ClassificationCode1
GMSS: Graph-Based Multi-Task Self-Supervised Learning for EEG Emotion RecognitionCode1
mulEEG: A Multi-View Representation Learning on EEG SignalsCode1
Interpretable EEG seizure prediction using a multiobjective evolutionary algorithmCode1
BEATS: An Open-Source, High-Precision, Multi-Channel EEG Acquisition Tool SystemCode1
Automated Parkinson's Disease Detection and Affective Analysis from Emotional EEG SignalsCode1
Priming Cross-Session Motor Imagery Classification with A Universal Deep Domain Adaptation FrameworkCode1
2021 BEETL Competition: Advancing Transfer Learning for Subject Independence & Heterogenous EEG Data SetsCode1
Towards Best Practice of Interpreting Deep Learning Models for EEG-based Brain Computer InterfacesCode1
PARSE: Pairwise Alignment of Representations in Semi-Supervised EEG Learning for Emotion RecognitionCode1
Advanced sleep spindle identification with neural networksCode1
Tensor-CSPNet: A Novel Geometric Deep Learning Framework for Motor Imagery ClassificationCode1
Domain-Invariant Representation Learning from EEG with Private EncodersCode1
Real-Time Seizure Detection using EEG: A Comprehensive Comparison of Recent Approaches under a Realistic SettingCode1
NEAR - Newborns EEG Artifact RemovalCode1
A Saliency based Feature Fusion Model for EEG Emotion EstimationCode1
ExBrainable: An Open-Source GUI for CNN-based EEG Decoding and Model InterpretationCode1
A 1D CNN for high accuracy classification and transfer learning in motor imagery EEG-based brain-computer interfaceCode1
A Deep Knowledge Distillation framework for EEG assisted enhancement of single-lead ECG based sleep stagingCode1
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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