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 501–525 of 1655 papers

TitleStatusHype
Calibration of off-the-shelf low-cost wearable EEG headset for application in field studies—0
Removal of Ocular Artifacts in EEG Using Deep Learning—0
Avoiding Post-Processing with Event-Based Detection in Biomedical SignalsCode0
Modeling cognitive load as a self-supervised brain rate with electroencephalography and deep learning—0
U-Sleep's resilience to AASM guidelines—0
EEG-Based Epileptic Seizure Prediction Using Temporal Multi-Channel Transformers—0
Weight-based Channel-model Matrix Framework provides a reasonable solution for EEG-based cross-dataset emotion recognition—0
Identification of Cognitive Workload during Surgical Tasks with Multimodal Deep Learning—0
Examining Uniqueness and Permanence of the WAY EEG GAL dataset toward User Authentication—0
Studying Drowsiness Detection Performance while Driving through Scalable Machine Learning Models using Electroencephalography—0
Machine Learning For Classification Of Antithetical Emotional States—0
Subject-independent trajectory prediction using pre-movement EEG during grasp and lift task—0
SaleNet: A low-power end-to-end CNN accelerator for sustained attention level evaluation using EEG—0
EEG-based Emotion Recognition via Efficient Convolutional Neural Network and Contrastive Learning—0
Classification of eye-state using EEG recordings: speed-up gains using signal epochs and mutual information measure—0
Classification of Electroencephalograms during Mathematical Calculations Using Deep Learning—0
Finding neural signatures for obesity through feature selection on source-localized EEG—0
Periodic and non-periodic brainwaves emerging via random syncronization of closed loops of firing neurons—0
Development of Sleep State Trend (SST), a bedside measure of neonatal sleep state fluctuations based on single EEG channels—0
EEG4Students: An Experimental Design for EEG Data Collection and Machine Learning AnalysisCode0
Too Fine or Too Coarse? The Goldilocks Composition of Data Complexity for Robust Left-Right Eye-Tracking ClassifiersCode0
Convolutional Neural Networks with A Topographic Representation Module for EEG-Based Brain-Computer Interfaces—0
Locally temporal-spatial pattern learning with graph attention mechanism for EEG-based emotion recognition—0
A Monotonicity Constrained Attention Module for Emotion Classification with Limited EEG DataCode0
EEG-BBNet: a Hybrid Framework for Brain Biometric using Graph Connectivity—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