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 1201–1225 of 1655 papers

TitleStatusHype
A framework for seizure detection using effective connectivity, graph theory and deep modular neural networks—0
Machine Learning Approaches for Detecting the Depression from Resting-State Electroencephalogram (EEG): A Review Study—0
Contextual Minimum-Norm Estimates (CMNE): A Deep Learning Method for Source Estimation in Neuronal Networks—0
Deep User Identification Model with Multiple Biometrics—0
Machine learning with electroencephalography features for precise diagnosis of depression subtypes—0
Localization of MEG and EEG Brain Signals by Alternating Projection—0
EEG Signal Dimensionality Reduction and Classification using Tensor Decomposition and Deep Convolutional Neural Networks—0
A Deep Evolutionary Approach to Bioinspired Classifier Optimisation for Brain-Machine Interaction—0
Towards Deep Learning-Based EEG Electrode Detection Using Automatically Generated Labels—0
Classification of Hand Movements from EEG using a Deep Attention-based LSTM Network—0
Large-scale directed network inference with multivariate transfer entropy and hierarchical statistical testing—0
A-Phase classification using convolutional neural networks—0
Adversarial Feature Learning in Brain Interfacing: An Experimental Study on Eliminating Drowsiness Effects—0
Synthetic Epileptic Brain Activities Using Generative Adversarial NetworksCode0
An end-to-end (deep) neural network applied to raw EEG, fNIRs and body motion data for data fusion and BCI classification task without any pre-/post-processing—0
Electroencephalography based Classification of Long-term Stress using Psychological Labeling—0
Deep Invertible Networks for EEG-based brain-signal decoding—0
Machine learning without a feature set for detecting bursts in the EEG of preterm infants—0
Deep Learning with ConvNET Predicts Imagery Tasks Through EEG—0
Suitability of an inter-burst detection method for grading hypoxic-ischemic encephalopathy in newborn EEG—0
Protecting Privacy of Users in Brain-Computer Interface Applications—0
Applying Transfer Learning To Deep Learned Models For EEG Analysis—0
Tensor Decomposition for EEG Signal Retrieval—0
Electroencephalogram (EEG) for Delineating Objective Measure of Autism Spectrum Disorder (ASD) (Extended Version)—0
Predicting epileptic seizures using nonnegative matrix factorizationCode0
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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