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 376–400 of 1655 papers

TitleStatusHype
Calibration of off-the-shelf low-cost wearable EEG headset for application in field studies—0
Mental arithmetic task classification with convolutional neural network based on spectral-temporal features from EEG—0
Removal of Ocular Artifacts in EEG Using Deep Learning—0
Evaluating Latent Space Robustness and Uncertainty of EEG-ML Models under Realistic Distribution ShiftsCode1
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
SleePyCo: Automatic Sleep Scoring with Feature Pyramid and Contrastive LearningCode1
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
clusterBMA: Bayesian model averaging for clusteringCode1
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
Transfer Learning of an Ensemble of DNNs for SSVEP BCI Spellers without User-Specific TrainingCode1
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
Periodic and non-periodic brainwaves emerging via random syncronization of closed loops of firing neurons—0
Finding neural signatures for obesity through feature selection on source-localized EEG—0
Decoding speech perception from non-invasive brain recordingsCode2
Development of Sleep State Trend (SST), a bedside measure of neonatal sleep state fluctuations based on single EEG channels—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