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 101–125 of 1655 papers

TitleStatusHype
EEG Channel Interpolation Using Deep Encoder-decoder NetwoksCode1
Investigating Brain Connectivity with Graph Neural Networks and GNNExplainerCode1
Cross Task Neural Architecture Search for EEG Signal ClassificationsCode1
Learning Generative Factors of EEG Data with Variational auto-encodersCode1
Correct block-design experiments mitigate temporal correlation bias in EEG classificationCode1
LGGNet: Learning from Local-Global-Graph Representations for Brain-Computer InterfaceCode1
clusterBMA: Bayesian model averaging for clusteringCode1
Decoding Covert Speech from EEG Using a Functional Areas Spatio-Temporal TransformerCode1
MASA-TCN: Multi-anchor Space-aware Temporal Convolutional Neural Networks for Continuous and Discrete EEG Emotion RecognitionCode1
MAtt: A Manifold Attention Network for EEG DecodingCode1
An IoT Endpoint System-on-Chip for Secure and Energy-Efficient Near-Sensor AnalyticsCode1
edBB: Biometrics and Behavior for Assessing Remote EducationCode1
An electronic neuromorphic system for real-time detection of High Frequency Oscillations (HFOs) in intracranial EEGCode1
Deep comparisons of Neural Networks from the EEGNet familyCode1
MS-MDA: Multisource Marginal Distribution Adaptation for Cross-subject and Cross-session EEG Emotion RecognitionCode1
mulEEG: A Multi-View Representation Learning on EEG SignalsCode1
Deep learning with convolutional neural networks for EEG decoding and visualizationCode1
NEAR - Newborns EEG Artifact RemovalCode1
Can Brain Signals Reveal Inner Alignment with Human Languages?Code1
BENDR: using transformers and a contrastive self-supervised learning task to learn from massive amounts of EEG dataCode1
Device JNEEG to convert Jetson Nano to brain-Computer interfaces. Short reportCode1
An Attention-Based Deep Learning Approach for Sleep Stage Classification With Single-Channel EEGCode1
A few filters are enough: Convolutional Neural Network for P300 DetectionCode1
Disguising Personal Identity Information in EEG SignalsCode1
EEG2Mel: Reconstructing Sound from Brain Responses to MusicCode1
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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