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 1201–1250 of 1655 papers

TitleStatusHype
A framework for seizure detection using effective connectivity, graph theory and deep modular neural networks—0
Machine Learning Approaches for Detecting the Depression from Resting-State Electroencephalogram (EEG): A Review Study—0
Contextual Minimum-Norm Estimates (CMNE): A Deep Learning Method for Source Estimation in Neuronal Networks—0
Deep User Identification Model with Multiple Biometrics—0
Machine learning with electroencephalography features for precise diagnosis of depression subtypes—0
Localization of MEG and EEG Brain Signals by Alternating Projection—0
EEG Signal Dimensionality Reduction and Classification using Tensor Decomposition and Deep Convolutional Neural Networks—0
A Deep Evolutionary Approach to Bioinspired Classifier Optimisation for Brain-Machine Interaction—0
Towards Deep Learning-Based EEG Electrode Detection Using Automatically Generated Labels—0
Classification of Hand Movements from EEG using a Deep Attention-based LSTM Network—0
Large-scale directed network inference with multivariate transfer entropy and hierarchical statistical testing—0
A-Phase classification using convolutional neural networks—0
Adversarial Feature Learning in Brain Interfacing: An Experimental Study on Eliminating Drowsiness Effects—0
Synthetic Epileptic Brain Activities Using Generative Adversarial NetworksCode0
An end-to-end (deep) neural network applied to raw EEG, fNIRs and body motion data for data fusion and BCI classification task without any pre-/post-processing—0
Electroencephalography based Classification of Long-term Stress using Psychological Labeling—0
Deep Invertible Networks for EEG-based brain-signal decoding—0
Machine learning without a feature set for detecting bursts in the EEG of preterm infants—0
Deep Learning with ConvNET Predicts Imagery Tasks Through EEG—0
Suitability of an inter-burst detection method for grading hypoxic-ischemic encephalopathy in newborn EEG—0
Protecting Privacy of Users in Brain-Computer Interface Applications—0
Applying Transfer Learning To Deep Learned Models For EEG Analysis—0
Tensor Decomposition for EEG Signal Retrieval—0
Electroencephalogram (EEG) for Delineating Objective Measure of Autism Spectrum Disorder (ASD) (Extended Version)—0
Predicting epileptic seizures using nonnegative matrix factorizationCode0
Visualizing High Dimensional Dynamical Processes—0
Cross-Subject Statistical Shift Estimation for Generalized Electroencephalography-based Mental Workload Assessment—0
Brain correlates of task-load and dementia elucidation with tensor machine learning using oddball BCI paradigm—0
Bayesian fusion and multimodal DCM for EEG and fMRI—0
Advancing Speech Recognition With No Speech Or With Noisy Speech—0
Speech Recognition With No Speech Or With Noisy Speech Beyond English—0
Robust End-to-End Speaker Verification Using EEG—0
Decoding P300 Variability using Convolutional Neural NetworksCode0
Modified swarm-based metaheuristics enhance Gradient Descent initialization performance: Application for EEG spatial filtering—0
Classification of EEG Signals using Genetic Programming for Feature Construction—0
The Dreem Headband as an Alternative to Polysomnography for EEG Signal Acquisition and Sleep Staging—0
Probabilistic Structure Learning for EEG/MEG Source Imaging with Hierarchical Graph Prior—0
On the use of Pairwise Distance Learning for Brain Signal Classification with Limited ObservationsCode0
Brain-Network Clustering via Kernel-ARMA Modeling and the Grassmannian—0
Automated Classification of Seizures against Nonseizures: A Deep Learning Approach—0
Amplifying the Neural Power Spectrum—0
Manifold-regression to predict from MEG/EEG brain signals without source modelingCode0
Simultaneous induction of SSMVEP and SMR Using a Gaiting video stimulus: a novel hybrid brain-computer interface—0
Adaptive Subspace Sampling for Class Imbalance Processing-Some clarifications, algorithm, and further investigation including applications to Brain Computer Interface—0
EEG Classification by factoring in Sensor Configuration—0
Utilizing Deep Learning Towards Multi-modal Bio-sensing and Vision-based Affective Computing—0
Discriminative Sleep Patterns of Alzheimer's Disease via Tensor Factorization—0
Classification of Perceived Human Stress using Physiological Signals—0
Emotion Classification in Response to Tactile Enhanced Multimedia using Frequency Domain Features of Brain Signals—0
A Novel Bi-hemispheric Discrepancy Model for EEG 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