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 1176–1200 of 1655 papers

TitleStatusHype
EEG-Based Driver Drowsiness Estimation Using Feature Weighted Episodic Training—0
Manifold Oblique Random Forests: Towards Closing the Gap on Convolutional Deep NetworksCode0
Sleep Stage Classification Using Bidirectional LSTM in Wearable Multi-sensor Systems—0
CogniVal: A Framework for Cognitive Word Embedding EvaluationCode0
Improved robust weighted averaging for event-related potentials in EEGCode0
Adversarial Representation Learning for Robust Patient-Independent Epileptic Seizure DetectionCode0
A few filters are enough: Convolutional Neural Network for P300 DetectionCode1
Carbogen inhalation during Non-Convulsive Status Epilepticus: A quantitative analysis of EEG recordings—0
Spoken Speech Enhancement using EEG—0
EEG based Emotion Recognition of Image Stimuli—0
Unravelling the neural signatures of dream recall in EEG: a deep learning approach—0
Tracking momentary attention fluctuations with an EEG-based cognitive brain-machine interface—0
HHHFL: Hierarchical Heterogeneous Horizontal Federated Learning for ElectroencephalographyCode0
Machine Learning Approaches for Detecting the Depression from Resting-State Electroencephalogram (EEG): A Review Study—0
A framework for seizure detection using effective connectivity, graph theory and deep modular neural networks—0
Contextual Minimum-Norm Estimates (CMNE): A Deep Learning Method for Source Estimation in Neuronal Networks—0
Deep User Identification Model with Multiple Biometrics—0
Localization of MEG and EEG Brain Signals by Alternating Projection—0
Machine learning with electroencephalography features for precise diagnosis of depression subtypes—0
EEG Signal Dimensionality Reduction and Classification using Tensor Decomposition and Deep Convolutional Neural Networks—0
A Deep Evolutionary Approach to Bioinspired Classifier Optimisation for Brain-Machine Interaction—0
Towards Deep Learning-Based EEG Electrode Detection Using Automatically Generated Labels—0
Classification of Hand Movements from EEG using a Deep Attention-based LSTM Network—0
Multi-task Generative Adversarial Learning on Geometrical Shape Reconstruction from EEG Brain SignalsCode1
Large-scale directed network inference with multivariate transfer entropy and hierarchical statistical testing—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