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 126–150 of 1655 papers

TitleStatusHype
EEGNet: A Compact Convolutional Network for EEG-based Brain-Computer InterfacesCode1
A Saliency based Feature Fusion Model for EEG Emotion EstimationCode1
Embedding Decomposition for Artifacts Removal in EEG SignalsCode1
Enhancing Low-Density EEG-Based Brain-Computer Interfaces with Similarity-Keeping Knowledge DistillationCode1
An IoT Endpoint System-on-Chip for Secure and Energy-Efficient Near-Sensor AnalyticsCode1
Artificial Intelligence for EEG Prediction: Applied Chaos TheoryCode1
ExBrainable: An Open-Source GUI for CNN-based EEG Decoding and Model InterpretationCode1
Evaluating Latent Space Robustness and Uncertainty of EEG-ML Models under Realistic Distribution ShiftsCode1
Extracting the Auditory Attention in a Dual-Speaker Scenario from EEG using a Joint CNN-LSTM ModelCode1
Real-time noise cancellation with Deep LearningCode1
A Transformer-based deep neural network model for SSVEP classificationCode1
Can Brain Signals Reveal Inner Alignment with Human Languages?Code1
Augmenting interictal mapping with neurovascular coupling biomarkers by structured factorization of epileptic EEG and fMRI dataCode1
Self-Supervised Graph Neural Networks for Improved Electroencephalographic Seizure AnalysisCode1
Decoding Covert Speech from EEG Using a Functional Areas Spatio-Temporal TransformerCode1
Automatic detection of microsleep episodes with deep learningCode1
BEATS: An Open-Source, High-Precision, Multi-Channel EEG Acquisition Tool SystemCode1
Evaluation of Interpretability for Deep Learning algorithms in EEG Emotion Recognition: A case study in AutismCode1
fMRI from EEG is only Deep Learning away: the use of interpretable DL to unravel EEG-fMRI relationshipsCode1
Brain Topography Adaptive Network for Satisfaction Modeling in Interactive Information Access SystemCode1
CwA-T: A Channelwise AutoEncoder with Transformer for EEG Abnormality DetectionCode1
IC-U-Net: A U-Net-based Denoising Autoencoder Using Mixtures of Independent Components for Automatic EEG Artifact RemovalCode1
MASA-TCN: Multi-anchor Space-aware Temporal Convolutional Neural Networks for Continuous and Discrete EEG Emotion RecognitionCode1
Investigating Brain Connectivity with Graph Neural Networks and GNNExplainerCode1
Subject-Aware Contrastive Learning for BiosignalsCode1
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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