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 801–850 of 1655 papers

TitleStatusHype
Mental State Classification Using Multi-graph Features—0
Mental State Recognition via Wearable EEG—0
Mental Workload Estimation with Electroencephalogram Signals by Combining Multi-Space Deep Models—0
MI-BMInet: An Efficient Convolutional Neural Network for Motor Imagery Brain--Machine Interfaces with EEG Channel Selection—0
MindArm: Mechanized Intelligent Non-Invasive Neuro-Driven Prosthetic Arm System—0
MindBigData 2022 A Large Dataset of Brain Signals—0
Mindfulness Improves Brain Computer Interface Performance by Increasing Control over Neural Activity in the Alpha Band—0
MindReading: An Ultra-Low-Power Photonic Accelerator for EEG-based Human Intention Recognition—0
Mind the beat: detecting audio onsets from EEG recordings of music listening—0
Mining the Mind: Linear Discriminant Analysis of MEG source reconstruction time series supports dynamic changes in deep brain regions during meditation sessions—0
Mixed Neural Network Approach for Temporal Sleep Stage Classification—0
Mobile EEG artifact correction on limited hardware using artifact subspace recon- struction—0
Model-Agnostic Meta-Learning for EEG Motor Imagery Decoding in Brain-Computer-Interfacing—0
Modeling and Mining Multi-Aspect Graphs With Scalable Streaming Tensor Decomposition—0
Modeling cognitive load as a self-supervised brain rate with electroencephalography and deep learning—0
Modeling the Complex Dynamics and Changing Correlations of Epileptic Events—0
Modelling Brain Connectivity Networks by Graph Embedding for Dyslexia Diagnosis—0
MODMA dataset: a Multi-modal Open Dataset for Mental-disorder Analysis—0
Monitoring fetal electroencephalogram intrapartum: a systematic literature review—0
Monitoring Trust in Human-Machine Interactions for Public Sector Applications—0
Motion Artifacts Correction from Single-Channel EEG and fNIRS Signals using Novel Wavelet Packet Decomposition in Combination with Canonical Correlation Analysis—0
Motor-Imagery-Based Brain Computer Interface using Signal Derivation and Aggregation Functions—0
Motor Imagery Classification based on CNN-GRU Network with Spatio-Temporal Feature Representation—0
Motor Imagery Classification of Single-Arm Tasks Using Convolutional Neural Network based on Feature Refining—0
Motor imagery classification using EEG spectrograms—0
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
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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