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 151–175 of 1655 papers

TitleStatusHype
Real-time noise cancellation with Deep LearningCode1
An IoT Endpoint System-on-Chip for Secure and Energy-Efficient Near-Sensor AnalyticsCode1
clusterBMA: Bayesian model averaging for clusteringCode1
MIN2Net: End-to-End Multi-Task Learning for Subject-Independent Motor Imagery EEG ClassificationCode1
MS-MDA: Multisource Marginal Distribution Adaptation for Cross-subject and Cross-session EEG Emotion RecognitionCode1
CognitionCapturer: Decoding Visual Stimuli From Human EEG Signal With Multimodal InformationCode1
Multi-task Generative Adversarial Learning on Geometrical Shape Reconstruction from EEG Brain SignalsCode1
NEAR - Newborns EEG Artifact RemovalCode1
CwA-T: A Channelwise AutoEncoder with Transformer for EEG Abnormality DetectionCode1
Correct block-design experiments mitigate temporal correlation bias in EEG classificationCode1
Cross Task Neural Architecture Search for EEG Signal ClassificationsCode1
fMRI from EEG is only Deep Learning away: the use of interpretable DL to unravel EEG-fMRI relationshipsCode1
A Knowledge Distillation Framework For Enhancing Ear-EEG Based Sleep Staging With Scalp-EEG DataCode1
Data augmentation for learning predictive models on EEG: a systematic comparisonCode1
Hypercomplex Multimodal Emotion Recognition from EEG and Peripheral Physiological SignalsCode1
K-EmoCon, a multimodal sensor dataset for continuous emotion recognition in naturalistic conversationsCode1
NeuroXAI: Adaptive, robust, explainable surrogate framework for determination of channel importance in EEG applicationCode1
Decoding Human Attentive States from Spatial-temporal EEG Patches Using TransformersCode1
Deep learning with convolutional neural networks for EEG decoding and visualizationCode1
Decoding Natural Images from EEG for Object RecognitionCode1
SleePyCo: Automatic Sleep Scoring with Feature Pyramid and Contrastive LearningCode1
Deep comparisons of Neural Networks from the EEGNet familyCode1
REST: Robust and Efficient Neural Networks for Sleep Monitoring in the WildCode1
Spatio-Temporal EEG Representation Learning on Riemannian Manifold and Euclidean SpaceCode1
U-Time: A Fully Convolutional Network for Time Series Segmentation Applied to 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