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

Showing 326350 of 1655 papers

TitleStatusHype
Granger Causality using Neural NetworksCode0
Decoding P300 Variability using Convolutional Neural NetworksCode0
Decision SincNet: Neurocognitive models of decision making that predict cognitive processes from neural signalsCode0
Dealing with Unknown Unknowns: Identification and Selection of Minimal Sensing for Fractional Dynamics with Unknown InputsCode0
Hybrid multi-objective evolutionary algorithm based on Search Manager framework for big data optimization problemsCode0
CARE-rCortex: a Matlab toolbox for the analysis of CArdio-REspiratory-related activity in the CortexCode0
Bayesian Inference on Brain-Computer Interfaces via GLASSCode0
Dataset combining EEG, eye-tracking, and high-speed video for ocular activity analysis across BCI paradigmsCode0
Cerebral Signal Instantaneous Parameters Estimation MATLAB Toolbox - User Guide Version 2.3Code0
Decoding Envelope and Frequency-Following EEG Responses to Continuous Speech Using Deep Neural NetworksCode0
Direct Estimation of Differential Functional Graphical ModelsCode0
Coupled Support Tensor Machine Classification for Multimodal Neuroimaging DataCode0
Converting Your Thoughts to Texts: Enabling Brain Typing via Deep Feature Learning of EEG SignalsCode0
A Framework to Evaluate Independent Component Analysis applied to EEG signal: testing on the Picard algorithmCode0
Convolutional Monge Mapping Normalization for learning on sleep dataCode0
Characterising Alzheimer's Disease with EEG-based Energy Landscape AnalysisCode0
Joint Learning of Full-structure Noise in Hierarchical Bayesian Regression ModelsCode0
Cross-validation in high-dimensional spaces: a lifeline for least-squares models and multi-class LDACode0
Concept-based explainability for an EEG transformer modelCode0
Learning from imperfect training data using a robust loss function: application to brain image segmentationCode0
Complex common spatial patterns on time-frequency decomposed EEG for brain-computer interfaceCode0
ChronoNet: A Deep Recurrent Neural Network for Abnormal EEG IdentificationCode0
A library of quantitative markers of seizure severityCode0
Conditional canonical correlation estimation based on covariates with random forestsCode0
Compact Convolutional Neural Networks for Classification of Asynchronous Steady-state Visual Evoked PotentialsCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1BiHDMAccuracy74.35Unverified
2DGCNNAccuracy69.88Unverified
3DBNAccuracy66.77Unverified
#ModelMetricClaimedVerifiedStatus
1MultitaskSSVEPAccuracy (5-fold)92.2Unverified
#ModelMetricClaimedVerifiedStatus
1DBNAccuracy86.08Unverified