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 176–200 of 1655 papers

TitleStatusHype
Device JNEEG to convert Jetson Nano to brain-Computer interfaces. Short reportCode1
ICLabel: An automated electroencephalographic independent component classifier, dataset, and websiteCode0
Image-based eeg classification of brain responses to song recordingsCode0
An Automated System for Epilepsy Detection using EEG Brain Signals based on Deep Learning ApproachCode0
Imagined speech classification using EEGCode0
HHHFL: Hierarchical Heterogeneous Horizontal Federated Learning for ElectroencephalographyCode0
High-dimensional Functional Graphical Model Structure Learning via Neighborhood Selection ApproachCode0
GraphSleepNet: Adaptive Spatial-Temporal Graph Convolutional Networks for Sleep Stage ClassificationCode0
Hybrid multi-objective evolutionary algorithm based on Search Manager framework for big data optimization problemsCode0
Improved robust weighted averaging for event-related potentials in EEGCode0
Generalized Concomitant Multi-Task Lasso for sparse multimodal regressionCode0
Adversarial Representation Learning for Robust Patient-Independent Epileptic Seizure DetectionCode0
Adversarial Filtering Based Evasion and Backdoor Attacks to EEG-Based Brain-Computer InterfacesCode0
gen2Out: Detecting and Ranking Generalized AnomaliesCode0
Granger Causality using Neural NetworksCode0
Analysis of EEG frequency bands for Envisioned Speech RecognitionCode0
FOIT: Fast Online Instance Transfer for Improved EEG Emotion RecognitionCode0
Feature Weighting and Regularization of Common Spatial Patterns in EEG-Based Motor Imagery BCICode0
FBDNN: Filter Banks and Deep Neural Networks for Portable and Fast Brain-Computer InterfacesCode0
From Euclidean to Riemannian Means: Information Geometry for SSVEP ClassificationCode0
Graph Convolutional Neural Networks for analysis of EEG signals, BCI applicationCode0
Inherent fuzzy entropy for the improvement of EEG complexity evaluationCode0
Exploring Embedding Methods in Binary Hyperdimensional Computing: A Case Study for Motor-Imagery based Brain-Computer InterfacesCode0
Exploiting Multiple EEG Data Domains with Adversarial LearningCode0
Explainable Data Poison Attacks on Human Emotion Evaluation Systems based on EEG SignalsCode0
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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