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 1–25 of 1655 papers

TitleStatusHype
NeuroXAI: Adaptive, robust, explainable surrogate framework for determination of channel importance in EEG applicationCode1
AFPM: Alignment-based Frame Patch Modeling for Cross-Dataset EEG Decoding—0
EEG Foundation Challenge: From Cross-Task to Cross-Subject EEG Decoding—0
Brain2Vec: A Deep Learning Framework for EEG-Based Stress Detection Using CNN-LSTM-Attention—0
Dataset combining EEG, eye-tracking, and high-speed video for ocular activity analysis across BCI paradigmsCode0
Channel-Imposed Fusion: A Simple yet Effective Method for Medical Time Series Classification—0
Advancing Brainwave Modeling with a Codebook-Based Foundation Model—0
EEG-Based Inter-Patient Epileptic Seizure Detection Combining Domain Adversarial Training with CNN-BiLSTM Network—0
Robust Emotion Recognition via Bi-Level Self-Supervised Continual Learning—0
Pretraining Large Brain Language Model for Active BCI: Silent Speech—0
A Simple Review of EEG Foundation Models: Datasets, Advancements and Future Perspectives—0
The use of Multi-domain Electroencephalogram Representations in the building of Models based on Convolutional and Recurrent Neural Networks for Epilepsy DetectionCode0
A Statistical Approach for Synthetic EEG Data GenerationCode0
Siamese Network with Dual Attention for EEG-Driven Social Learning: Bridging the Human-Robot Gap in Long-Tail Autonomous Driving—0
Artifact detection and localization in single-channel mobile EEG for sleep research using deep learning and attention mechanisms—0
Classification of ADHD and Healthy Children Using EEG Based Multi-Band Spatial Features Enhancement—0
Optimized Feature Selection and Neural Network-Based Classification of Motor Imagery Using EEG Signals—0
Decoding Covert Speech from EEG Using a Functional Areas Spatio-Temporal TransformerCode1
Chirp Localization via Fine-Tuned Transformer Model: A Proof-of-Concept Study—0
EEG-CLIP : Learning EEG representations from natural language descriptionsCode1
Spatial Distillation based Distribution Alignment (SDDA) for Cross-Headset EEG ClassificationCode1
Insights into Schizophrenia: Leveraging Machine Learning for Early Identification via EEG, ERP, and Demographic Attributes—0
Cross-Subject Depression Level Classification Using EEG Signals with a Sample Confidence Method—0
Toward Scalable Access to Neurodevelopmental Screening: Insights, Implementation, and Challenges—0
Music Therapy based Stress Prediction using Homological Feature Analysis on EEG Signals—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