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 1601–1625 of 1655 papers

TitleStatusHype
Common Spatial Generative Adversarial Networks based EEG Data Augmentation for Cross-Subject Brain-Computer Interface—0
Community Detection in Multi-frequency EEG Networks—0
Comparative Analysis of Epileptic Seizure Prediction: Exploring Diverse Pre-Processing Techniques and Machine Learning Models—0
Comparison of Attention-based Deep Learning Models for EEG Classification—0
Comparison of EEG based epilepsy diagnosis using neural networks and wavelet transform—0
Comparison of inverse problem linear and non-linear methods for localization source: a combined TMS-EEG study—0
Comparison of the P300 detection accuracy related to the BCI speller and image recognition scenarios—0
Compensated Integrated Gradients to Reliably Interpret EEG Classification—0
Complex Emotion Recognition System using basic emotions via Facial Expression, EEG, and ECG Signals: a review—0
Complexity Measures for Quantifying Changes in Electroencephalogram in Alzheimers Disease—0
Compressed Sensing for Energy-Efficient Wireless Telemonitoring: Challenges and Opportunities—0
Compressed Sensing of EEG for Wireless Telemonitoring with Low Energy Consumption and Inexpensive Hardware—0
Compressed Sensing of Multi-Channel EEG Signals: The Simultaneous Cosparsity and Low Rank Optimization—0
Accurate and efficient Simulation of very high-dimensional Neural Mass Models with distributed-delay Connectome Tensors—0
Computing extracellular electric potentials from neuronal simulations—0
Conex-Connect: Learning Patterns in Extremal Brain Connectivity From Multi-Channel EEG Data—0
Confidence-Aware Subject-to-Subject Transfer Learning for Brain-Computer Interface—0
Constrained Variational Autoencoder for improving EEG based Speech Recognition Systems—0
Contextual Minimum-Norm Estimates (CMNE): A Deep Learning Method for Source Estimation in Neuronal Networks—0
Continuous ErrP detections during multimodal human-robot interaction—0
Continuous monitoring of cognitive load using advanced computerized analysis of brain signals during virtual simulator training for laparoscopic surgery, reflects laparoscopic dexterity. A comparative study using a novel wireless device—0
Continuous Speech Recognition using EEG and Video—0
ConTraNet: A single end-to-end hybrid network for EEG-based and EMG-based human machine interfaces—0
Contrastive Learning of Shared Spatiotemporal EEG Representations Across Individuals for Naturalistic Neuroscience—0
Contrastive Learning of Subject-Invariant EEG Representations for Cross-Subject Emotion Recognition—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