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 1251–1275 of 1655 papers

TitleStatusHype
Attention-based Transfer Learning for Brain-computer Interface—0
End-to-end Sleep Staging with Raw Single Channel EEG using Deep Residual ConvNetsCode0
A Novel Task-Oriented Text Corpus in Silent Speech Recognition and its Natural Language Generation Construction Method—0
Signal2Image Modules in Deep Neural Networks for EEG ClassificationCode0
Classification of Two-channel Signals by Means of Genetic Programming—0
Hierarchical Deep Feature Learning For Decoding Imagined Speech From EEG—0
Deep Learning the EEG Manifold for Phonological Categorization from Active Thoughts—0
Human Intracranial EEG Quantitative Analysis and Automatic Feature Learning for Epileptic Seizure Prediction—0
Advancing NLP with Cognitive Language Processing SignalsCode0
A Many Objective Optimization Approach for Transfer Learning in EEG Classification—0
Recognition of Advertisement Emotions with Application to Computational Advertising—0
Understanding language-elicited EEG data by predicting it from a fine-tuned language model—0
On the Vulnerability of CNN Classifiers in EEG-Based BCIs—0
Information Theoretic Feature Transformation Learning for Brain Interfaces—0
Cortical Mirror-System Activation During Real-Life Game Playing: An Intracranial Electroencephalography (EEG) Study—0
Adversarial Deep Learning in EEG Biometrics—0
Machine learning approaches in Detecting the Depression from Resting-state Electroencephalogram (EEG): A Review Study—0
The role of physiological complexity changes in resting-state EEG in clinical effectiveness of rTMS and tDCS in treatments of resistant depression—0
An Ensemble Learning Based Classification of Individual Finger Movement from EEG—0
A Novel Independent RNN Approach to Classification of Seizures against Non-seizures—0
Classification of EEG-Based Brain Connectivity Networks in Schizophrenia Using a Multi-Domain Connectome Convolutional Neural Network—0
Early Detection of Mental Stress Using Advanced Neuroimaging and Artificial Intelligence—0
Residual Deep Convolutional Neural Network for EEG Signal Classification in Epilepsy—0
Machine Learning for removing EEG artifacts: Setting the benchmark—0
A semi-supervised deep learning algorithm for abnormal EEG identification—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