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
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
Transfer Learning for Brain-Computer Interfaces: A Euclidean Space Data Alignment ApproachCode1
Transfer Learning Enhanced Common Spatial Pattern Filtering for Brain Computer Interfaces (BCIs): Overview and a New Approach—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
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
Ripple oscillations in the left temporal neocortex are associated with impaired verbal episodic memory encoding—0
Dynamical Component Analysis (DyCA): Dimensionality Reduction For High-Dimensional Deterministic Time-Series—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
A deep learning architecture to detect events in EEG signals during sleepCode0
Affective EEG-Based Person Identification Using the Deep Learning Approach—0
Addressing Class Imbalance in Classification Problems of Noisy Signals by using Fourier Transform Surrogates—0
Semi-supervised Seizure Prediction with Generative Adversarial Networks—0
Improving brain computer interface performance by data augmentation with conditional Deep Convolutional Generative Adversarial Networks—0
Fast and Accurate Multiclass Inference for MI-BCIs Using Large Multiscale Temporal and Spectral FeaturesCode0
A large-scale evaluation framework for EEG deep learning architectures—0
Denoising Time Series Data Using Asymmetric Generative Adversarial Networks—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