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 701–725 of 1655 papers

TitleStatusHype
Efficient Video Summarization Framework using EEG and Eye-tracking Signals—0
EGNN-C+: Interpretable Evolving Granular Neural Network and Application in Classification of Weakly-Supervised EEG Data Streams—0
Benchmarking real-time algorithms for in-phase auditory stimulation of low amplitude slow waves with wearable EEG devices during sleep—0
Detecting Epileptic Seizures from EEG Data using Neural Networks—0
Electroencephalogram (EEG) for Delineating Objective Measure of Autism Spectrum Disorder (ASD) (Extended Version)—0
Electroencephalogram Sensor Data Compression Using An Asymmetrical Sparse Autoencoder With A Discrete Cosine Transform Layer—0
Electroencephalogram Signal Processing with Independent Component Analysis and Cognitive Stress Classification using Convolutional Neural Networks—0
Electroencephalography and mild cognitive impairment research: A scoping review and bibliometric analysis (ScoRBA)—0
Electroencephalography based Classification of Long-term Stress using Psychological Labeling—0
Electroencephalography signal processing based on textural features for monitoring the driver's state by a Brain-Computer Interface—0
Electromagnetic Source Imaging via a Data-Synthesis-Based Convolutional Encoder-Decoder Network—0
Electromyogram (EMG) Removal by Adding Sources of EMG (ERASE) -- A novel ICA-based algorithm for removing myoelectric artifacts from EEG -- Part 2—0
Electromyogram (EMG) Removal by Adding Sources of EMG (ERASE) -- A novel ICA-based algorithm for removing myoelectric artifacts from EEG -- Part 1—0
Electrophysiological indicators of gesture perception—0
Detecting Driver's Distraction using Long-term Recurrent Convolutional Network—0
Classification of Brainwave Signals Based on Hybrid Deep Learning and an Evolutionary Algorithm—0
Emergence of Brain Rhythms: Model Interpretation of EEG Data—0
Classification of Distraction Levels Using Hybrid Deep Neural Networks From EEG Signals—0
Benchmarking Domain Generalization on EEG-based Emotion Recognition—0
Emotion Detection from EEG using Transfer Learning—0
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
Emotion-Inspired Deep Structure (EiDS) for EEG Time Series Forecasting—0
Emotion Recognition with Machine Learning Using EEG Signals—0
Emotion Recognition With Temporarily Localized 'Emotional Events' in Naturalistic Context—0
A Generalised Seizure Prediction with Convolutional Neural Networks for Intracranial and Scalp Electroencephalogram Data Analysis—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