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 151–175 of 1655 papers

TitleStatusHype
EEG-CLIP : Learning EEG representations from natural language descriptionsCode1
An IoT Endpoint System-on-Chip for Secure and Energy-Efficient Near-Sensor AnalyticsCode1
Artificial Intelligence for EEG Prediction: Applied Chaos TheoryCode1
EEGdenoiseNet: A benchmark dataset for end-to-end deep learning solutions of EEG denoisingCode1
A Transformer-based deep neural network model for SSVEP classificationCode1
EEG-Inception: An Accurate and Robust End-to-End Neural Network for EEG-based Motor Imagery ClassificationCode1
EEGNet: A Compact Convolutional Network for EEG-based Brain-Computer InterfacesCode1
EEG Synthetic Data Generation Using Probabilistic Diffusion ModelsCode1
2021 BEETL Competition: Advancing Transfer Learning for Subject Independence & Heterogenous EEG Data SetsCode1
Real-time noise cancellation with Deep LearningCode1
ExBrainable: An Open-Source GUI for CNN-based EEG Decoding and Model InterpretationCode1
Extracting Different Levels of Speech Information from EEG Using an LSTM-Based ModelCode1
A Knowledge Distillation Framework For Enhancing Ear-EEG Based Sleep Staging With Scalp-EEG DataCode1
Federated Transfer Learning for EEG Signal ClassificationCode1
Self-Supervised Graph Neural Networks for Improved Electroencephalographic Seizure AnalysisCode1
GMSS: Graph-Based Multi-Task Self-Supervised Learning for EEG Emotion RecognitionCode1
A Compact and Interpretable Convolutional Neural Network for Cross-Subject Driver Drowsiness Detection from Single-Channel EEGCode1
IC-U-Net: A U-Net-based Denoising Autoencoder Using Mixtures of Independent Components for Automatic EEG Artifact RemovalCode1
Interpretable SincNet-based Deep Learning for Emotion Recognition from EEG brain activityCode1
Causal Recurrent Variational Autoencoder for Medical Time Series GenerationCode1
Deep Multi-Task Learning for SSVEP Detection and Visual Response MappingCode1
Enhancing Low-Density EEG-Based Brain-Computer Interfaces with Similarity-Keeping Knowledge DistillationCode1
HNPE: Leveraging Global Parameters for Neural Posterior EstimationCode1
LGGNet: Learning from Local-Global-Graph Representations for Brain-Computer InterfaceCode1
U-Time: A Fully Convolutional Network for Time Series Segmentation Applied to Sleep StagingCode1
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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