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 1051–1075 of 1655 papers

TitleStatusHype
Sample Dominance Aware Framework via Non-Parametric Estimation for Spontaneous Brain-Computer Interface—0
ScalingNet: extracting features from raw EEG data for emotion recognition—0
Scatter-based common spatial patterns -- a unified spatial filtering framework—0
SCFNet:A Transferable IIIC EEG Classification Network—0
Schizophrenia detection based on EEG using Recurrent Auto-Encoder framework—0
SCORE-IT: A Machine Learning-based Tool for Automatic Standardization of EEG Reports—0
SCRIB: Set-classifier with Class-specific Risk Bounds for Blackbox Models—0
Second Order Bilinear Discriminant Analysis for single trial EEG analysis—0
See What You See: Self-supervised Cross-modal Retrieval of Visual Stimuli from Brain Activity—0
Seizure Classification of EEG based on Wavelet Signal Denoising Using a Novel Channel Selection Algorithm—0
Epileptic Seizure Classification Using Combined Labels and a Genetic Algorithm—0
Seizure Detection using Least EEG Channels by Deep Convolutional Neural Network—0
Seizure pathways and seizure durations can vary independently within individual patients with focal epilepsy—0
Seizure Prediction Using Bidirectional LSTM—0
Selection of Proper EEG Channels for Subject Intention Classification Using Deep Learning—0
CLASH: Contrastive learning through alignment shifting to extract stimulus information from EEG—0
Self-supervised Learning for Electroencephalogram: A Systematic Survey—0
Semantic-aware Contrastive Learning for Electroencephalography-to-Text Generation with Curriculum Learning—0
Semi-automated Annotation of Signal Events in Clinical EEG Data—0
Semi-supervised Seizure Prediction with Generative Adversarial Networks—0
Semi-supervised Stacked Label Consistent Autoencoder for Reconstruction and Analysis of Biomedical Signals—0
Sensoring and Application of Multimodal Data for the Detection of Freezing of Gait in Parkinson's Disease—0
Sequential Best-Arm Identification with Application to Brain-Computer Interface—0
SFE-Net: EEG-based Emotion Recognition with Symmetrical Spatial Feature Extraction—0
Siamese Network with Dual Attention for EEG-Driven Social Learning: Bridging the Human-Robot Gap in Long-Tail Autonomous Driving—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