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 1076–1100 of 1655 papers

TitleStatusHype
Identifying Stable Patterns over Time for Emotion Recognition from EEG—0
Identifying trace alternant activity in neonatal EEG using an inter-burst detection approach—0
Images from the Mind: BCI image evolution based on Rapid Serial Visual Presentation of polygon primitives—0
Imaging the time series of one single referenced EEG electrode for Epileptic Seizures Risk Analysis—0
Impact of the reference choice on scalp EEG connectivity estimation—0
Implementation of Deep Neural Networks to Classify EEG Signals using Gramian Angular Summation Field for Epilepsy Diagnosis—0
Implementation of tools for lessening the influence of artifacts in EEG signal analysis—0
Importance Weighting with a Adversarial Network for Large-Scale Sleep Staging—0
EEG Classification by factoring in Sensor Configuration—0
Improved EEG Event Classification Using Differential Energy—0
Improved Explanatory Efficacy on Human Affect and Workload through Interactive Process in Artificial Intelligence—0
Improved Motor Imagery Classification Using Adaptive Spatial Filters Based on Particle Swarm Optimization Algorithm—0
Improvement of Resting-state EEG Analysis Process with Spectrum Weight-Voting based on LES—0
Improving auditory attention decoding performance of linear and non-linear methods using state-space model—0
Improving brain computer interface performance by data augmentation with conditional Deep Convolutional Generative Adversarial Networks—0
Improving classification accuracy of feedforward neural networks for spiking neuromorphic chips—0
Improving EEG based Continuous Speech Recognition—0
Improving EEG based continuous speech recognition using GAN—0
Improving EEG Classification Through Randomly Reassembling Original and Generated Data with Transformer-based Diffusion Models—0
Improving EEG Decoding via Clustering-based Multi-task Feature Learning—0
Improving J-divergence of brain connectivity states by graph Laplacian denoising—0
Improving P300 Speller performance by means of optimization and machine learning—0
Improving self-supervised pretraining models for epileptic seizure detection from EEG data—0
Anchored-STFT and GNAA: An extension of STFT in conjunction with an adversarial data augmentation technique for the decoding of neural signals—0
Improving the spectral resolution of fMRI signals through the temporal de-correlation approach—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