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 551–575 of 1655 papers

TitleStatusHype
Mental State Classification Using Multi-graph Features—0
Novel techniques for improving NNetEn entropy calculation for short and noisy time series—0
Validating an SVM-based neonatal seizure detection algorithm for generalizability, non-inferiority and clinical efficacy—0
An Evaluation of the EEG alpha-to-theta and theta-to-alpha band Ratios as Indexes of Mental Workload—0
Automated Parkinson's Disease Detection and Affective Analysis from Emotional EEG SignalsCode1
DGAFF: Deep Genetic Algorithm Fitness Formation for EEG Bio-Signal Channel Selection—0
Enhancing Affective Representations of Music-Induced EEG through Multimodal Supervision and latent Domain AdaptationCode0
Wavelet-Based Multi-Class Seizure Type Classification System—0
Priming Cross-Session Motor Imagery Classification with A Universal Deep Domain Adaptation FrameworkCode1
Low Latency Real-Time Seizure Detection Using Transfer Deep Learning—0
Raspberry PI Shield - for measure EEG (PIEEG)Code2
2021 BEETL Competition: Advancing Transfer Learning for Subject Independence & Heterogenous EEG Data SetsCode1
Significant Low-dimensional Spectral-temporal Features for Seizure Detection—0
Towards Best Practice of Interpreting Deep Learning Models for EEG-based Brain Computer InterfacesCode1
Grasp-and-Lift Detection from EEG Signal Using Convolutional Neural Network—0
PARSE: Pairwise Alignment of Representations in Semi-Supervised EEG Learning for Emotion RecognitionCode1
Dimensional criterion for forecasting nonlinear systems by reservoir computing—0
Spectrally Adaptive Common Spatial Patterns—0
Efficacy of Transformer Networks for Classification of Raw EEG Data—0
Inter-subject Contrastive Learning for Subject Adaptive EEG-based Visual RecognitionCode2
AI-based artistic representation of emotions from EEG signals: a discussion on fairness, inclusion, and aestheticsCode0
Advanced sleep spindle identification with neural networksCode1
Tensor-CSPNet: A Novel Geometric Deep Learning Framework for Motor Imagery ClassificationCode1
An Olfactory EEG Signal Classification Network Based on Frequency Band Feature Extraction—0
Brain-Computer-Interface controlled robot via RaspberryPi and PiEEGCode2
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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