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 526–550 of 1655 papers

TitleStatusHype
Complexity Measures for Quantifying Changes in Electroencephalogram in Alzheimers Disease—0
Complex Emotion Recognition System using basic emotions via Facial Expression, EEG, and ECG Signals: a review—0
Assessing Rate limits Using Behavioral and Neural Responses of Interaural-Time-Difference Cues in Fine-Structure and Envelope—0
Compensated Integrated Gradients to Reliably Interpret EEG Classification—0
Assessing learned features of Deep Learning applied to EEG—0
A multi-level interpretable sleep stage scoring system by infusing experts' knowledge into a deep network architecture—0
Comparison of the P300 detection accuracy related to the BCI speller and image recognition scenarios—0
Comparison of inverse problem linear and non-linear methods for localization source: a combined TMS-EEG study—0
Comparison of EEG based epilepsy diagnosis using neural networks and wavelet transform—0
Comparison of Attention-based Deep Learning Models for EEG Classification—0
Assessing Functional Neural Connectivity as an Indicator of Cognitive Performance—0
A Multi-Context Character Prediction Model for a Brain-Computer Interface—0
Advancing Brainwave Modeling with a Codebook-Based Foundation Model—0
Comparative Analysis of Epileptic Seizure Prediction: Exploring Diverse Pre-Processing Techniques and Machine Learning Models—0
A SPA-based Manifold Learning Framework for Motor Imagery EEG Data Classification—0
Community Detection in Multi-frequency EEG Networks—0
A single channel sleep-spindle detector based on multivariate classification of EEG epochs: MUSSDET.—0
A Multi Constrained Transformer-BiLSTM Guided Network for Automated Sleep Stage Classification from Single-Channel EEG—0
Common Spatial Generative Adversarial Networks based EEG Data Augmentation for Cross-Subject Brain-Computer Interface—0
Comment on "On the Extraction of Purely Motor EEG Neural Correlates during an Upper Limb Visuomotor Task"—0
A Simple Review of EEG Foundation Models: Datasets, Advancements and Future Perspectives—0
Combining Generative and Discriminative Neural Networks for Sleep Stages Classification—0
Combining Euclidean Alignment and Data Augmentation for BCI decoding—0
A simple EEG-based decision tool for neonatal therapeutic hypothermia in hypoxic-ischemic encephalopathy—0
A Multi-channel EEG Data Analysis for Poor Neuro-prognostication in Comatose Patients with Self and Cross-channel Attention Mechanism—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