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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 926–950 of 1655 papers

TitleStatusHype
Is the brain macroscopically linear? A system identification of resting state dynamicsCode0
Resting-state EEG sex classification using selected brain connectivity representation—0
Improving J-divergence of brain connectivity states by graph Laplacian denoising—0
Automatic detection of abnormal EEG signals using wavelet feature extraction and gradient boosting decision tree—0
Fluctuations in EEG band power at subject-specific timescales over minutes to days explain changes in seizure evolutions—0
Light-Weight 1-D Convolutional Neural Network Architecture for Mental Task Identification and Classification Based on Single-Channel EEG—0
Improving EEG Decoding via Clustering-based Multi-task Feature Learning—0
Quantifying Synchronization in a Biologically Inspired Neural Network—0
Automatic Micro-sleep Detection under Car-driving Simulation Environment using Night-sleep EEG—0
Predicting the Transition from Short-term to Long-term Memory based on Deep Neural Network—0
Cross-Correlation Based Discriminant Criterion for Channel Selection in Motor Imagery BCI Systems—0
Comparison of Attention-based Deep Learning Models for EEG Classification—0
A Generative Model to Synthesize EEG Data for Epileptic Seizure Prediction—0
Statistical control for spatio-temporal MEG/EEG source imaging with desparsified mutli-task Lasso—0
Edge Deep Learning for Neural Implants—0
Anchored-STFT and GNAA: An extension of STFT in conjunction with an adversarial data augmentation technique for the decoding of neural signals—0
Semi-Supervised Learning for Sparsely-Labeled Sequential Data: Application to Healthcare Video ProcessingCode0
Conditional canonical correlation estimation based on covariates with random forestsCode0
Deep Learning in EEG: Advance of the Last Ten-Year Critical Period—0
Patient-independent Epileptic Seizure Prediction using Deep Learning Models—0
Patient-Specific Seizure Prediction Using Single Seizure Electroencephalography Recording—0
Deep learning-based classification of fine hand movements from low frequency EEG—0
REPAC: Reliable estimation of phase-amplitude coupling in brain networks—0
Overlapping neural representations for the position of visible and imagined objects—0
Correlation based Multi-phasal models for improved imagined speech EEG recognition—0
Show:102550
← PrevPage 38 of 67Next →
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