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 1601–1625 of 1655 papers

TitleStatusHype
Spatiotemporal Emotion Recognition using Deep CNN Based on EEG during Music ListeningCode0
CogAlign: Learning to Align Textual Neural Representations to Cognitive Language Processing SignalsCode0
Synthetic Epileptic Brain Activities Using Generative Adversarial NetworksCode0
Deep Convolutional Neural Network Applied to Electroencephalography: Raw Data vs Spectral FeaturesCode0
EnK: Encoding time-information in convolutionCode0
Applying advanced machine learning models to classify electro-physiological activity of human brain for use in biometric identificationCode0
Large Transformers are Better EEG LearnersCode0
Neuroadaptive electroencephalography: a proof-of-principle study in infantsCode0
Enhancing Affective Representations of Music-Induced EEG through Multimodal Supervision and latent Domain AdaptationCode0
Decoding P300 Variability using Convolutional Neural NetworksCode0
ToFFi -- Toolbox for Frequency-based Fingerprinting of Brain SignalsCode0
RIGOLETTO -- RIemannian GeOmetry LEarning: applicaTion To cOnnectivity. A contribution to the Clinical BCI Challenge -- WCCI2020Code0
Learning from imperfect training data using a robust loss function: application to brain image segmentationCode0
Refining ADHD diagnosis with EEG: The impact of preprocessing and temporal segmentation on classification accuracyCode0
CARE-rCortex: a Matlab toolbox for the analysis of CArdio-REspiratory-related activity in the CortexCode0
Learning Representations from EEG with Deep Recurrent-Convolutional Neural NetworksCode0
Learning Robust Features using Deep Learning for Automatic Seizure DetectionCode0
Decoding kinetic features of hand motor preparation from single‐trial EEG using convolutional neural networksCode0
Q-EEGNet: an Energy-Efficient 8-bit Quantized Parallel EEGNet Implementation for Edge Motor-Imagery Brain--Machine InterfacesCode0
SeizureNet: Multi-Spectral Deep Feature Learning for Seizure Type ClassificationCode0
Decoding Envelope and Frequency-Following EEG Responses to Continuous Speech Using Deep Neural NetworksCode0
Semi-Supervised Learning for Sparsely-Labeled Sequential Data: Application to Healthcare Video ProcessingCode0
Enhanced Cross-Dataset Electroencephalogram-based Emotion Recognition using Unsupervised Domain AdaptationCode0
End-to-end Sleep Staging with Raw Single Channel EEG using Deep Residual ConvNetsCode0
A Statistical Approach for Synthetic EEG Data GenerationCode0
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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