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 301–325 of 1655 papers

TitleStatusHype
Deep Optimal Transport for Domain Adaptation on SPD ManifoldsCode0
Dynamical Embedding of Single Channel Electroencephalogram for Artifact Subspace ReconstructionCode0
Exploring Embedding Methods in Binary Hyperdimensional Computing: A Case Study for Motor-Imagery based Brain-Computer InterfacesCode0
Deep Convolutional Neural Network Applied to Electroencephalography: Raw Data vs Spectral FeaturesCode0
Deep Feature Learning for EEG RecordingsCode0
Decoding P300 Variability using Convolutional Neural NetworksCode0
Deep learning-based electroencephalography analysis: a systematic reviewCode0
Decoding Envelope and Frequency-Following EEG Responses to Continuous Speech Using Deep Neural NetworksCode0
Efficient Hierarchical Bayesian Inference for Spatio-temporal Regression Models in NeuroimagingCode0
Decision SincNet: Neurocognitive models of decision making that predict cognitive processes from neural signalsCode0
An Open-source Toolbox for Analysing and Processing PhysioNet Databases in MATLAB and OctaveCode0
End-to-end Sleep Staging with Raw Single Channel EEG using Deep Residual ConvNetsCode0
Adaptive Modality Balanced Online Knowledge Distillation for Brain-Eye-Computer based Dim Object DetectionCode0
Enhancing Affective Representations of Music-Induced EEG through Multimodal Supervision and latent Domain AdaptationCode0
Decoding kinetic features of hand motor preparation from single‐trial EEG using convolutional neural networksCode0
Enriching Large-Scale Eventuality Knowledge Graph with Entailment RelationsCode0
Deep Learning Human Mind for Automated Visual ClassificationCode0
Deep Jansen-Rit Parameter Inference for Model-Driven Analysis of Brain ActivityCode0
Cross-validation in high-dimensional spaces: a lifeline for least-squares models and multi-class LDACode0
Cueless EEG imagined speech for subject identification: dataset and benchmarksCode0
Dataset combining EEG, eye-tracking, and high-speed video for ocular activity analysis across BCI paradigmsCode0
Convolutional Monge Mapping Normalization for learning on sleep dataCode0
Coupled Support Tensor Machine Classification for Multimodal Neuroimaging DataCode0
Bayesian Inference on Brain-Computer Interfaces via GLASSCode0
Converting Your Thoughts to Texts: Enabling Brain Typing via Deep Feature Learning of EEG SignalsCode0
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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