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 1351–1375 of 1655 papers

TitleStatusHype
Automatic Electrodes Detection during simultaneous EEG/fMRI acquisition—0
Characterizing functional brain networks and emotional centers based on Rasa theory of Indian aesthetics—0
EEG-based Subjects Identification based on Biometrics of Imagined Speech using EMD—0
Convolutional Neural Network Approach for EEG-based Emotion Recognition using Brain Connectivity and its Spatial Information—0
EEG-based video identification using graph signal modeling and graph convolutional neural network—0
Evaluation of Preference of Multimedia Content using Deep Neural Networks for Electroencephalography—0
Localization of Brain Activity from EEG/MEG Using MV-PURE FrameworkCode0
Knowledge extraction, modeling and formalization: EEG case study—0
Classification of grasping tasks based on EEG-EMG coherence—0
Sleep Stage Classification: Scalability Evaluations of Distributed Approaches—0
Towards Asynchronous Motor Imagery-Based Brain-Computer Interfaces: a joint training scheme using deep learning—0
Intracerebral EEG Artifact Identification Using Convolutional Neural NetworksCode0
A Novel Method for Epileptic Seizure Detection Using Coupled Hidden Markov Models—0
Spatial Filtering for Brain Computer Interfaces: A Comparison between the Common Spatial Pattern and Its Variant—0
Active Learning for Regression Using Greedy SamplingCode0
Transfer Learning Enhanced Common Spatial Pattern Filtering for Brain Computer Interfaces (BCIs): Overview and a New Approach—0
Deep Transfer Learning for EEG-based Brain Computer Interface—0
Classification of EEG Signal based on non-Gaussian Neutral Vector—0
Dynamic reshaping of functional brain networks during visual object recognition—0
Dynamical Component Analysis (DyCA): Dimensionality Reduction For High-Dimensional Deterministic Time-Series—0
Ripple oscillations in the left temporal neocortex are associated with impaired verbal episodic memory encoding—0
Multimodal Classification with Deep Convolutional-Recurrent Neural Networks for Electroencephalography—0
Cross-paradigm pretraining of convolutional networks improves intracranial EEG decoding—0
Spatio-Temporal Structured Sparse Regression with Hierarchical Gaussian Process Priors—0
A hybrid automated detection of epileptic seizures in EEG based on wavelet and machine learning techniques—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