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 251–275 of 1655 papers

TitleStatusHype
Tuning to non-veridical features in attention and perceptual decision-making—0
Investigating the role of visual experience with face-masks in face recognition during COVID-19—0
Approximately optimal domain adaptation with Fisher's Linear Discriminant—0
Comparison and Analysis of Cognitive Load under 2D/3D Visual Stimuli—0
Partial Label Learning for Emotion Recognition from EEGCode1
Score-Based Data Generation for EEG Spatial Covariance Matrices: Towards Boosting BCI PerformanceCode1
Electrode Clustering and Bandpass Analysis of EEG Data for Gaze Estimation—0
Sleep Model -- A Sequence Model for Predicting the Next Sleep Stage—0
Novel Epileptic Seizure Detection Techniques and their Empirical Analysis—0
Deep comparisons of Neural Networks from the EEGNet familyCode1
Temporal stability of intracranial EEG abnormality maps for localising epileptogenic tissue—0
Classification of BCI-EEG based on augmented covariance matrixCode0
Explainable Label-flipping Attacks on Human Emotion Assessment System—0
Data Poisoning Attacks on EEG Signal-based Risk Assessment Systems—0
First steps towards quantum machine learning applied to the classification of event-related potentials—0
Relating EEG to continuous speech using deep neural networks: a review—0
Biophysical aspects of neurocognitive modeling with long-term sustained temperature variations—0
The Past, Current, and Future of Neonatal Intensive Care Units with Artificial Intelligence—0
Variational Autoencoder Learns Better Feature Representations for EEG-based Obesity Classification—0
Approximating DTW with a convolutional neural network on EEG data—0
Cross-Subject Deep Transfer Models for Evoked Potentials in Brain-Computer Interface—0
Semantic-aware Contrastive Learning for Electroencephalography-to-Text Generation with Curriculum Learning—0
Device JNEEG to convert Jetson Nano to brain-Computer interfaces. Short reportCode1
Deep Learning for scalp High Frequency Oscillations Identification—0
Interpretable Classification of Early Stage Parkinson's Disease from EEG—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