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 326–350 of 1655 papers

TitleStatusHype
A Multi-channel EEG Data Analysis for Poor Neuro-prognostication in Comatose Patients with Self and Cross-channel Attention Mechanism—0
A Dynamic Domain Adaptation Deep Learning Network for EEG-based Motor Imagery Classification—0
A Knowledge-Driven Cross-view Contrastive Learning for EEG Representation—0
SCVCNet: Sliding cross-vector convolution network for cross-task and inter-individual-set EEG-based cognitive workload recognitionCode0
A Multi Constrained Transformer-BiLSTM Guided Network for Automated Sleep Stage Classification from Single-Channel EEG—0
Electroencephalogram Sensor Data Compression Using An Asymmetrical Sparse Autoencoder With A Discrete Cosine Transform Layer—0
mEBAL2 Database and Benchmark: Image-based Multispectral Eyeblink DetectionCode0
Sleep Stage Classification Using a Pre-trained Deep Learning Model—0
Early warning indicators via latent stochastic dynamical systems—0
Real-Time Non-Invasive Imaging and Detection of Spreading Depolarizations through EEG: An Ultra-Light Explainable Deep Learning Approach—0
RoBoSS: A Robust, Bounded, Sparse, and Smooth Loss Function for Supervised LearningCode0
A Human-Machine Joint Learning Framework to Boost Endogenous BCI Training—0
EOG Artifact Removal from Single and Multi-channel EEG Recordings through the combination of Long Short-Term Memory Networks and Independent Component Analysis—0
State-transition dynamics of resting-state functional magnetic resonance imaging data: Model comparison and test-to-retest analysisCode0
Functional Graph Contrastive Learning of Hyperscanning EEG Reveals Emotional Contagion Evoked by Stereotype-Based Stressors—0
Large Transformers are Better EEG LearnersCode0
A Hybrid Deep Spatio-Temporal Attention-Based Model for Parkinson's Disease Diagnosis Using Resting State EEG Signals—0
Comparative Analysis of Epileptic Seizure Prediction: Exploring Diverse Pre-Processing Techniques and Machine Learning Models—0
EEG-based Cognitive Load Classification using Feature Masked Autoencoding and Emotion Transfer Learning—0
Concept-based explainability for an EEG transformer modelCode0
Mental Workload Estimation with Electroencephalogram Signals by Combining Multi-Space Deep Models—0
Perturbing a Neural Network to Infer Effective Connectivity: Evidence from Synthetic EEG Data—0
Deep Generative Models for Physiological Signals: A Systematic Literature Review—0
Trends in Machine Learning and Electroencephalogram (EEG): A Review for Undergraduate Researchers—0
Classification of sleep stages from EEG, EOG and EMG signals by SSNet—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