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 226–250 of 1655 papers

TitleStatusHype
Multimodal Brain-Computer Interface for In-Vehicle Driver Cognitive Load Measurement: Dataset and Baselines—0
Knowledge-Distilled Graph Neural Networks for Personalized Epileptic Seizure Detection—0
Optimized EEG based mood detection with signal processing and deep neural networks for brain-computer interface—0
An embedding for EEG signals learned using a triplet loss—0
Are uGLAD? Time will tell!Code0
Optimized preprocessing and Tiny ML for Attention State Classification—0
Towards Domain Generalization for ECG and EEG Classification: Algorithms and BenchmarksCode1
Relate auditory speech to EEG by shallow-deep attention-based network—0
Picture-word interference in language production studies: Exploring the roles of attention and processing times—0
Detecting post-stroke aphasia using EEG-based neural envelope tracking of natural speech—0
Recovering Arrhythmic EEG Transients from Their Stochastic Interference—0
Challenges facing the explainability of age prediction models: case study for two modalitiesCode0
Relating EEG recordings to speech using envelope tracking and the speech-FFR—0
Scope and Arbitration in Machine Learning Clinical EEG ClassificationCode0
Assessing gender fairness in EEG-based machine learning detection of Parkinson's disease: A multi-center studyCode0
Sliced-Wasserstein on Symmetric Positive Definite Matrices for M/EEG SignalsCode0
Depression Diagnosis and Drug Response Prediction via Recurrent Neural Networks and Transformers Utilizing EEG Signals—0
Inter-brain substrates of role switching during mother-child interaction—0
Scatter-based common spatial patterns -- a unified spatial filtering framework—0
A topological classifier to characterize brain states: When shape matters more than variance—0
EEG Synthetic Data Generation Using Probabilistic Diffusion ModelsCode1
Neural complexity -- Statistical-mechanical approach of human electroencephalograms—0
Online functional connectivity analysis of large all-to-all networks—0
One step closer to EEG based eye tracking—0
Machine Learning-Based Detection of Parkinson's Disease From Resting-State EEG: A Multi-Center Study—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