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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 826–850 of 1655 papers

TitleStatusHype
MP-SeizNet: A Multi-Path CNN Bi-LSTM Network for Seizure-Type Classification Using EEG—0
MRNet: a Multi-scale Residual Network for EEG-based Sleep Staging—0
MuBiNN: Multi-Level Binarized Recurrent Neural Network for EEG signal Classification—0
Multi-Branch Mutual-Distillation Transformer for EEG-Based Seizure Subtype Classification—0
Six-center Assessment of CNN-Transformer with Belief Matching Loss for Patient-independent Seizure Detection in EEG—0
Multichannel Synthetic Preictal EEG Signals to Enhance the Prediction of Epileptic Seizures—0
Multiclass Common Spatial Pattern for EEG based Brain Computer Interface with Adaptive Learning Classifier—0
Multi-feature classifiers for burst detection in single EEG channels from preterm infants—0
Multi-level Binarized LSTM in EEG Classification for Wearable Devices—0
Multilinear Low-Rank Tensors on Graphs & Applications—0
Multimodal Brain-Computer Interface for In-Vehicle Driver Cognitive Load Measurement: Dataset and Baselines—0
Multimodal Classification with Deep Convolutional-Recurrent Neural Networks for Electroencephalography—0
Multi-modal Cross-domain Self-supervised Pre-training for fMRI and EEG Fusion—0
Multimodal deep learning approach for joint EEG-EMG data compression and classification—0
Multimodal Affective States Recognition Based on Multiscale CNNs and Biologically Inspired Decision Fusion Model—0
Multimodal Emotion Recognition Using Multimodal Deep Learning—0
Multimodal Fusion of EMG and Vision for Human Grasp Intent Inference in Prosthetic Hand Control—0
Multimodal Identification of Alzheimer's Disease: A Review—0
Multimodal pathophysiological dataset of gradual cerebral ischemia in a cohort of juvenile pigs—0
Multimodal Physiological Signals Representation Learning via Multiscale Contrasting for Depression Recognition—0
Multi-Modal Wireless Flexible Gel-Free Sensors with Edge Deep Learning for Detecting and Alerting Freezing of Gait in Parkinson's Patients—0
Multi-Person Brain Activity Recognition via Comprehensive EEG Signal Analysis—0
Multiscale Fractal Analysis on EEG Signals for Music-Induced Emotion Recognition—0
Multi-Scale Neural network for EEG Representation Learning in BCI—0
Multiscale Wavelet Transfer Entropy with Application to Corticomuscular Coupling Analysis—0
Show:102550
← PrevPage 34 of 67Next →
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