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 451–475 of 1655 papers

TitleStatusHype
Cross-Correlation Based Discriminant Criterion for Channel Selection in Motor Imagery BCI Systems—0
Cross-individual Recognition of Emotions by a Dynamic Entropy based on Pattern Learning with EEG features—0
Cross-paradigm pretraining of convolutional networks improves intracranial EEG decoding—0
Canine EEG Helps Human: Cross-Species and Cross-Modality Epileptic Seizure Detection via Multi-Space Alignment—0
Cross-Subject Deep Transfer Models for Evoked Potentials in Brain-Computer Interface—0
Cross-Subject Depression Level Classification Using EEG Signals with a Sample Confidence Method—0
Cross-Subject Domain Adaptation for Classifying Working Memory Load with Multi-Frame EEG Images—0
Cross-Subject Statistical Shift Estimation for Generalized Electroencephalography-based Mental Workload Assessment—0
Convolutional Neural Networks for Automatic Detection of Artifacts from Independent Components Represented in Scalp Topographies of EEG Signals—0
Convolutional Neural Network Approach for EEG-based Emotion Recognition using Brain Connectivity and its Spatial Information—0
A Tale of Single-channel Electroencephalogram: Devices, Datasets, Signal Processing, Applications, and Future Directions—0
Adaptive Subspace Sampling for Class Imbalance Processing-Some clarifications, algorithm, and further investigation including applications to Brain Computer Interface—0
A Survey and Tutorial of EEG-Based Brain Monitoring for Driver State Analysis—0
DASPS: A Database for Anxious States based on a Psychological Stimulation—0
Data Augmentation for Enhancing EEG-based Emotion Recognition with Deep Generative Models—0
Contrastive Learning of Subject-Invariant EEG Representations for Cross-Subject Emotion Recognition—0
Contrastive Learning of Shared Spatiotemporal EEG Representations Across Individuals for Naturalistic Neuroscience—0
Cross-Subject Data Splitting for Brain-to-Text Decoding—0
Data-driven cortical clustering to provide a family of plausible solutions to M/EEG inverse problem—0
Data Poisoning Attacks on EEG Signal-based Risk Assessment Systems—0
A study of resting-state EEG biomarkers for depression recognition—0
An Accurate EEGNet-based Motor-Imagery Brain-Computer Interface for Low-Power Edge Computing—0
Advancing Speech Synthesis using EEG—0
Decline of long-range temporal correlations in the human brain during sustained wakefulness—0
A Computationally Efficient Multiclass Time-Frequency Common Spatial Pattern Analysis on EEG Motor Imagery—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