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 1026–1050 of 1655 papers

TitleStatusHype
Fusion of EEG and Musical Features in Continuous Music-emotion Recognition—0
Fuzzy temporal convolutional neural networks in P300-based Brain-computer interface for smart home interaction—0
Genetic algorithm for feature selection of EEG heterogeneous data—0
Gamma band oscillations reflect sensory and affective dimensions of pain—0
GANSER: A Self-supervised Data Augmentation Framework for EEG-based Emotion Recognition—0
Gated Recurrent Networks for Seizure Detection—0
Generating EEG features from Acoustic features—0
Generating Ten BCI Commands Using Four Simple Motor Imageries—0
Generative Adversarial Networks Conditioned by Brain Signals—0
Generative Autoencoder Kernels on Deep Learning for Brain Activity Analysis—0
Geometric feature performance under downsampling for EEG classification tasks—0
Geometry-Constrained EEG Channel Selection for Brain-Assisted Speech Enhancement—0
Global dynamics of neural mass models—0
Grading the severity of hypoxic-ischemic encephalopathy in newborn EEG using a convolutional neural network—0
Gradual Relation Network: Decoding Intuitive Upper Extremity Movement Imaginations Based on Few-Shot EEG Learning—0
Granger Causality in Multi-variate Time Series using a Time Ordered Restricted Vector Autoregressive Model—0
Graph Structure Based Data Augmentation Method—0
Grasp-and-Lift Detection from EEG Signal Using Convolutional Neural Network—0
Group Feature Learning and Domain Adversarial Neural Network for aMCI Diagnosis System Based on EEG—0
Group level MEG/EEG source imaging via optimal transport: minimum Wasserstein estimates—0
HDTorch: Accelerating Hyperdimensional Computing with GP-GPUs for Design Space Exploration—0
Head-related Impulse Response Cues for Spatial Auditory Brain-computer Interface—0
Hearables: Ear EEG Based Driver Fatigue Detection—0
HemCNN: Deep Learning enables decoding of fNIRS cortical signals in hand grip motor tasks—0
Hierarchical Deep Feature Learning For Decoding Imagined Speech 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