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 126–150 of 1655 papers

TitleStatusHype
CLARE: Cognitive Load Assessment in REaltime with Multimodal Data—0
EEG-Deformer: A Dense Convolutional Transformer for Brain-computer InterfacesCode2
Robust EEG-based Emotion Recognition Using an Inception and Two-sided Perturbation Model—0
NeuroNet: A Novel Hybrid Self-Supervised Learning Framework for Sleep Stage Classification Using Single-Channel EEGCode2
Clinical translation of machine learning algorithms for seizure detection in scalp electroencephalography: systematic review—0
Fusing Pretrained ViTs with TCNet for Enhanced EEG Regression—0
A simple EEG-based decision tool for neonatal therapeutic hypothermia in hypoxic-ischemic encephalopathy—0
MindArm: Mechanized Intelligent Non-Invasive Neuro-Driven Prosthetic Arm System—0
Synthesizing EEG Signals from Event-Related Potential Paradigms with Conditional Diffusion ModelsCode1
Identifying Attention-Deficit/Hyperactivity Disorder through the electroencephalogram complexity—0
EEGDiR: Electroencephalogram denoising network for temporal information storage and global modeling through Retentive NetworkCode0
Reconstructing Visual Stimulus Images from EEG Signals Based on Deep Visual Representation Model—0
Physics-informed and Unsupervised Riemannian Domain Adaptation for Machine Learning on Heterogeneous EEG Datasets—0
FAST functional connectivity implicates P300 connectivity in working memory deficits in Alzheimer's disease—0
EEG classifier cross-task transfer to avoid training sessions in robot-assisted rehabilitation—0
EGNN-C+: Interpretable Evolving Granular Neural Network and Application in Classification of Weakly-Supervised EEG Data Streams—0
Contrastive Learning of Shared Spatiotemporal EEG Representations Across Individuals for Naturalistic Neuroscience—0
Review of algorithms for predicting fatigue using EEG—0
Real-time EEG-based Emotion Recognition Model using Principal Component Analysis and Tree-based Models for NeurohumanitiesCode0
Subject-Independent Deep Architecture for EEG-based Motor Imagery Classification—0
Multiview Graph Learning with Consensus Graph—0
Epilepsy Seizure Detection and Prediction using an Approximate Spiking Convolutional Transformer—0
Self-supervised Learning for Electroencephalogram: A Systematic Survey—0
Multi-Source Domain Adaptation with Transformer-based Feature Generation for Subject-Independent EEG-based Emotion Recognition—0
3D-CLMI: A Motor Imagery EEG Classification Model via Fusion of 3D-CNN and LSTM with Attention—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