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 426–450 of 1655 papers

TitleStatusHype
Vector-Based Data Improves Left-Right Eye-Tracking Classifier Performance After a Covariate Distributional ShiftCode0
Six-center Assessment of CNN-Transformer with Belief Matching Loss for Patient-independent Seizure Detection in EEG—0
Significant changes in EEG neural oscillations during different phases of three-dimensional multiple object tracking task (3D-MOT) imply different roles for attention and working memory—0
EEG2Mel: Reconstructing Sound from Brain Responses to MusicCode1
A Hybrid Complex-valued Neural Network Framework with Applications to Electroencephalogram (EEG)—0
A Two-Stage Efficient 3-D CNN Framework for EEG Based Emotion Recognition—0
Time Majority Voting, a PC-based EEG Classifier for Non-expert UsersCode0
Continuous ErrP detections during multimodal human-robot interaction—0
LETS-GZSL: A Latent Embedding Model for Time Series Generalized Zero Shot Learning—0
TRUST-LAPSE: An Explainable and Actionable Mistrust Scoring Framework for Model MonitoringCode0
Comment on "On the Extraction of Purely Motor EEG Neural Correlates during an Upper Limb Visuomotor Task"—0
Correntropy-Based Logistic Regression with Automatic Relevance Determination for Robust Sparse Brain Activity Decoding—0
Reliability and validity of TMS-EEG biomarkers—0
Upper Limb Movement Recognition utilising EEG and EMG Signals for Rehabilitative RoboticsCode0
Human Brains Can't Detect Fake News: A Neuro-Cognitive Study of Textual Disinformation Susceptibility—0
EEG2Vec: Learning Affective EEG Representations via Variational Autoencoders—0
Toward reliable signals decoding for electroencephalogram: A benchmark study to EEGNeXCode1
Self-supervised Group Meiosis Contrastive Learning for EEG-Based Emotion RecognitionCode1
A multi-level interpretable sleep stage scoring system by infusing experts' knowledge into a deep network architecture—0
Schizophrenia detection based on EEG using Recurrent Auto-Encoder framework—0
Dreamento: an open-source dream engineering toolbox for sleep EEG wearablesCode1
Relating the fundamental frequency of speech with EEG using a dilated convolutional network—0
Task-oriented Self-supervised Learning for Anomaly Detection in ElectroencephalographyCode1
Learning Subject-Invariant Representations from Speech-Evoked EEG Using Variational Autoencoders—0
Automatic Sleep Scoring from Large-scale Multi-channel Pediatric EEGCode0
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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