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 1101–1125 of 1655 papers

TitleStatusHype
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
Spatial-Temporal Recurrent Neural Network for Emotion Recognition—0
Generative AI Enables EEG Super-Resolution via Spatio-Temporal Adaptive Diffusion Learning—0
Spatio-Temporal Analysis of Transformer based Architecture for Attention Estimation from EEG—0
Spatiotemporal Sparse Bayesian Learning with Applications to Compressed Sensing of Multichannel Physiological Signals—0
Spatio-temporal Spike and Slab Priors for Multiple Measurement Vector Problems—0
Spatio-Temporal Structured Sparse Regression with Hierarchical Gaussian Process Priors—0
Speaker Identification using EEG—0
Spectral independent component analysis with noise modeling for M/EEG source separation—0
Spectrally Adaptive Common Spatial Patterns—0
Spectro Temporal EEG Biomarkers For Binary Emotion Classification—0
Speech Artifact Removal from EEG Recordings of Spoken Word Production with Tensor Decomposition—0
Speech Recognition using EEG signals recorded using dry electrodes—0
Speech Recognition with no speech or with noisy speech—0
Speech Recognition With No Speech Or With Noisy Speech Beyond English—0
Speech Synthesis using EEG—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