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 1401–1425 of 1655 papers

TitleStatusHype
WU-NEAT: A clinically validated, open- source MATLAB toolbox for limited-channel neonatal EEG analysis—0
Intracranial Error Detection via Deep Learning—0
Classification of Epileptic EEG Signals by Wavelet based CFC—0
Covariate Shift Estimation based Adaptive Ensemble Learning for Handling Non-Stationarity in Motor Imagery related EEG-based Brain-Computer Interface—0
Classification of auditory stimuli from EEG signals with a regulated recurrent neural network reservoir—0
A machine learning model for identifying cyclic alternating patterns in the sleeping brain—0
Automated EEG-based Screening of Depression Using Deep Convolutional Neural Network—0
Organization and Independence or Interdependence? Study of the Neurophysiological Dynamics of Syntactic and Semantic Processing—0
Real-time fMRI neurofeedback training of the amygdala activity with simultaneous EEG in veterans with combat-related PTSD—0
Feature Weighting and Regularization of Common Spatial Patterns in EEG-Based Motor Imagery BCICode0
Cross-validation in high-dimensional spaces: a lifeline for least-squares models and multi-class LDACode0
Re-thinking EEG-based non-invasive brain interfaces: modeling and analysis—0
Epileptic Seizure Detection: A Deep Learning Approach—0
EEG emotion recognition using dynamical graph convolutional neural networks—0
Removing Confounding Factors Associated Weights in Deep Neural Networks Improves the Prediction Accuracy for Healthcare ApplicationsCode0
A Robust AUC Maximization Framework with Simultaneous Outlier Detection and Feature Selection for Positive-Unlabeled Classification—0
EEG machine learning with Higuchi fractal dimension and Sample Entropy as features for successful detection of depression—0
A Technique Based on Chaos for Brain Computer Interfacing—0
On the Variability of Chaos Indices in Sleep EEG Signals—0
Compact Convolutional Neural Networks for Classification of Asynchronous Steady-state Visual Evoked PotentialsCode0
Dealing with Unknown Unknowns: Identification and Selection of Minimal Sensing for Fractional Dynamics with Unknown InputsCode0
A single channel sleep-spindle detector based on multivariate classification of EEG epochs: MUSSDET.—0
Measuring Consciousness—0
Multiclass Common Spatial Pattern for EEG based Brain Computer Interface with Adaptive Learning Classifier—0
Directional and Causal Information Flow in EEG for Assessing Perceived Audio Quality—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