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 1401–1450 of 1655 papers

TitleStatusHype
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
Epileptic Seizure Detection: A Deep Learning Approach—0
Re-thinking EEG-based non-invasive brain interfaces: modeling and analysis—0
Cross-validation in high-dimensional spaces: a lifeline for least-squares models and multi-class LDACode0
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
On the Variability of Chaos Indices in Sleep EEG Signals—0
A Technique Based on Chaos for Brain Computer Interfacing—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
Reduced integration and improved segregation of functional brain networks in Alzheimer's disease—0
Multi-View Bayesian Correlated Component Analysis—0
ChronoNet: A Deep Recurrent Neural Network for Abnormal EEG IdentificationCode0
Extension of the non-parametric cluster-based time-frequency statistics to the full time windows and to single condition tests—0
Anticipating epileptic seizures through the analysis of EEG synchronization as a data classification problem—0
To jump or not to jump: The Bereitschaftspotential required to jump into 192-meter abyss—0
A Data Driven Approach for Resting-state EEG signal Classification of Schizophrenia with Control Participants using Random Matrix Theory—0
Improvement of Resting-state EEG Analysis Process with Spectrum Weight-Voting based on LES—0
Plasma Brain Dynamics (PBD): A Mechanism for EEG Waves Under Human Consciousness—0
An Automated System for Epilepsy Detection using EEG Brain Signals based on Deep Learning ApproachCode0
Time Series Segmentation through Automatic Feature Learning—0
Brain EEG Time Series Selection: A Novel Graph-Based Approach for Classification—0
Context tree selection for functional dataCode0
Deep Classification of Epileptic Signals—0
Optimizing Channel Selection for Seizure Detection—0
An Analysis of Two Common Reference Points for EEGs—0
Semi-automated Annotation of Signal Events in Clinical EEG Data—0
Gated Recurrent Networks for Seizure Detection—0
The Temple University Hospital Seizure Detection Corpus—0
Improved EEG Event Classification Using Differential Energy—0
Objective evaluation metrics for automatic classification of EEG events—0
Deep Architectures for Automated Seizure Detection in Scalp EEGs—0
Automatic Analysis of EEGs Using Big Data and Hybrid Deep Learning Architectures—0
Report: Dynamic Eye Movement Matching and Visualization Tool in Neuro GestureCode0
Towards Deep Modeling of Music Semantics using EEG Regularizers—0
Single-trial P300 Classification using PCA with LDA, QDA and Neural Networks—0
Targeting EEG/LFP Synchrony with Neural Nets—0
EEG-GRAPH: A Factor-Graph-Based Model for Capturing Spatial, Temporal, and Observational Relationships in Electroencephalograms—0
Subject Selection on a Riemannian Manifold for Unsupervised Cross-subject Seizure Detection—0
EEG-assisted Modulation of Sound Sources in the Auditory Scene—0
Riemannian tangent space mapping and elastic net regularization for cost-effective EEG markers of brain atrophy in Alzheimer's disease—0
Post-hoc labeling of arbitrary EEG recordings for data-efficient evaluation of neural decoding methods—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