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 826–850 of 1655 papers

TitleStatusHype
Single-channel EEG features during n-back task correlate with working memory load—0
BCGGAN: Ballistocardiogram artifact removal in simultaneous EEG-fMRI using generative adversarial network—0
Single-trial P300 Classification using PCA with LDA, QDA and Neural Networks—0
Sleep Analytics and Online Selective Anomaly Detection—0
Sleep Model -- A Sequence Model for Predicting the Next Sleep Stage—0
SLEEPNET: Automated Sleep Staging System via Deep Learning—0
Sleep Stage Classification: Scalability Evaluations of Distributed Approaches—0
Sleep Stage Classification Using Bidirectional LSTM in Wearable Multi-sensor Systems—0
Sleep Stage Classification Using a Pre-trained Deep Learning Model—0
Sleep Staging Based on Multi Scale Dual Attention Network—0
SleepTransformer: Automatic Sleep Staging with Interpretability and Uncertainty Quantification—0
Sonic Entanglements with Electromyography: Between Bodies, Signals, and Representations—0
SOUL: An Energy-Efficient Unsupervised Online Learning Seizure Detection Classifier—0
Source Aware Deep Learning Framework for Hand Kinematic Reconstruction using EEG Signal—0
Sparse algorithms for EEG source localization—0
Sparse Bayesian Learning for EEG Source Localization—0
Sparse Dynamical Features generation, application to Parkinson's Disease diagnosis—0
Sparsity-based Correction of Exponential Artifacts—0
Sparsity-Driven EEG Channel Selection for Brain-Assisted Speech Enhancement—0
Sparsity Enables Estimation of both Subcortical and Cortical Activity from MEG and EEG—0
Spatial Filtering for Brain Computer Interfaces: A Comparison between the Common Spatial Pattern and Its Variant—0
Spatial Filtering for EEG-Based Regression Problems in Brain-Computer Interface (BCI)—0
Spatial Filtering Pipeline Evaluation of Cortically Coupled Computer Vision System for Rapid Serial Visual Presentation—0
Spatial Neural Networks and their Functional Samples: Similarities and Differences—0
Spatial-Spectral Boosting Analysis for Stroke Patients' Motor Imagery EEG in Rehabilitation Training—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