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 801–825 of 1655 papers

TitleStatusHype
Learning Signal Representations for EEG Cross-Subject Channel Selection and Trial Classification—0
EEG-GNN: Graph Neural Networks for Classification of Electroencephalogram (EEG) Signals—0
Towards Long-term Non-invasive Monitoring for Epilepsy via Wearable EEG Devices—0
Cross-Subject Domain Adaptation for Classifying Working Memory Load with Multi-Frame EEG Images—0
BRAIN2DEPTH: Lightweight CNN Model for Classification of Cognitive States from EEG Recordings—0
Wheelchair automation by a hybrid BCI system using SSVEP and eye blinks—0
CogAlign: Learning to Align Textual Neural Representations to Cognitive Language Processing SignalsCode0
Artifact Detection and Correction in EEG data: A Review—0
Neuroadaptive electroencephalography: a proof-of-principle study in infantsCode0
Subject-Independent Brain-Computer Interface for Decoding High-Level Visual Imagery Tasks—0
Subject Independent Emotion Recognition using EEG Signals Employing Attention Driven Neural Networks—0
A highly scalable repository of waveform and vital signs data from bedside monitoring devices—0
EEG changes and motor deficits in Parkinson's disease patients: Correlation of motor scales and EEG power bands—0
Random Forest classifier for EEG-based seizure prediction—0
Tracé alternant detector for grading hypoxic-ischemic encephalopathy in neonatal EEG—0
Bioelectrical brain activity can predict prosocial behavior—0
Generating Ten BCI Commands Using Four Simple Motor Imageries—0
Neonatal seizure detection from raw multi-channel EEG using a fully convolutional architecture—0
Deep Learning for EEG Seizure Detection in Preterm Infants—0
SleepTransformer: Automatic Sleep Staging with Interpretability and Uncertainty Quantification—0
Automated Detection of Abnormalities from an EEG Recording of Epilepsy Patients With a Compact Convolutional Neural Network—0
Deep Correlation Analysis for Audio-EEG Decoding—0
Predicting speech intelligibility from EEG in a non-linear classification paradigm—0
Dyadic aggregated autoregressive (DASAR) model for time-frequency representation of biomedical signals—0
Deep Convolutional Neural Network Applied to Electroencephalography: Raw Data vs Spectral FeaturesCode0
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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