AIMC Topic: Electroencephalography

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Computational modeling of the effects of EEG volume conduction on functional connectivity metrics. Application to Alzheimer's disease continuum.

Journal of neural engineering Oct 29, 2019
OBJECTIVE: The aim of this study was to evaluate the effect of electroencephalographic (EEG) volume conduction in different measures of functional connectivity and to characterize the EEG coupling alterations at the different stages of dementia due t...
Humans Neural Networks, Computer Aged, 80 and over Male Electroencephalography Alzheimer Disease Brain Female Aged Nerve Net Models, Anatomic
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A hierarchical sequential neural network with feature fusion for sleep staging based on EOG and RR signals.

Journal of neural engineering Oct 29, 2019
OBJECTIVE: Currently, the automatic sleep staging methods mainly face two problems: the first problem is that although the algorithms which use electroencephalogram (EEG) signals perform well, acquiring EEG signals is complicated and uncomfortable; t...
Sleep Apnea Syndromes Electrooculography Sleep Stages Adult Male Electroencephalography Databases, Factual Humans Female Neural Networks, Computer
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Incorporating feature selection methods into a machine learning-based neonatal seizure diagnosis.

Medical hypotheses Oct 28, 2019
The present study developed a feature selection (FS)-based decision support system using the electroencephalography (EEG) signals recorded from neonates with and without seizures. The study employed 10 different FS algorithms to reduce the classifica...
Diagnosis, Computer-Assisted Decision Support Systems, Clinical Cluster Analysis Algorithms Signal Processing, Computer-Assisted Electroencephalography Infant, Newborn False Positive Reactions Seizures Sensitivity and Specificity Humans Machine Learning
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Convolutional neural networks for decoding of covert attention focus and saliency maps for EEG feature visualization.

Journal of neural engineering Oct 23, 2019
OBJECTIVE: Convolutional neural networks (CNNs) have proven successful as function approximators and have therefore been used for classification problems including electroencephalography (EEG) signal decoding for brain-computer interfaces (BCI). Arti...
Neural Networks, Computer Brain Mapping Humans Female Electroencephalography Male Attention
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An Exploration of Machine Learning Methods for Robust Boredom Classification Using EEG and GSR Data.

Sensors (Basel, Switzerland) Oct 20, 2019
In recent years, affective computing has been actively researched to provide a higher level of emotion-awareness. Numerous studies have been conducted to detect the user's emotions from physiological data. Among a myriad of target emotions, boredom, ...
Boredom Discriminant Analysis Electroencephalography Galvanic Skin Response Surveys and Questionnaires ROC Curve Area Under Curve Humans Machine Learning Adult Female Male Young Adult
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Spiking Neural Networks applied to the classification of motor tasks in EEG signals.

Neural networks : the official journal of the International Neural Network Society Oct 16, 2019
Motivated by the recent progress of Spiking Neural Network (SNN) models in pattern recognition, we report on the development and evaluation of brain signal classifiers based on SNNs. The work shows the capabilities of this type of Spiking Neurons in ...
Electroencephalography Imagination Psychomotor Performance Brain-Computer Interfaces Humans Neural Networks, Computer Brain
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An Ensemble Learning Approach for Electrocardiogram Sensor Based Human Emotion Recognition.

Sensors (Basel, Switzerland) Oct 16, 2019
Recently, researchers in the area of biosensor based human emotion recognition have used different types of machine learning models for recognizing human emotions. However, most of them still lack the ability to recognize human emotions with higher c...
Heart Rate Emotions Electroencephalography Signal Processing, Computer-Assisted Humans Machine Learning Algorithms Electrocardiography Support Vector Machine
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A novel hybrid deep learning scheme for four-class motor imagery classification.

Journal of neural engineering Oct 16, 2019
OBJECTIVE: Learning the structures and unknown correlations of a motor imagery electroencephalogram (MI-EEG) signal is important for its classification. It is also a major challenge to obtain good classification accuracy from the increased number of ...
Deep Learning Humans Neural Networks, Computer Electroencephalography Movement Brain Imagination
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A strategy combining intrinsic time-scale decomposition and a feedforward neural network for automatic seizure detection.

Physiological measurement Sep 30, 2019
UNLABELLED: Epilepsy is a common neurological disorder which can occur in people of all ages globally. For the clinical treatment of epileptic patients, the detection of epileptic seizures is of great significance.
Automation Humans Time Factors Seizures Neural Networks, Computer Electroencephalography Signal Processing, Computer-Assisted
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Orthogonal convolutional neural networks for automatic sleep stage classification based on single-channel EEG.

Computer methods and programs in biomedicine Sep 27, 2019
BACKGROUND AND OBJECTIVE: In recent years, several automatic sleep stage classification methods based on convolutional neural networks (CNN) by learning hierarchical feature representation automatically from raw EEG data have been proposed. However, ...
Calibration Adult Sleep Stages Algorithms Signal Processing, Computer-Assisted Time Factors Sensitivity and Specificity Humans Polysomnography Databases, Factual Sleep Apnea, Obstructive Electrocardiography Male Reproducibility of Results Neural Networks, Computer Sleep Wake Disorders Sleep Electroencephalography Machine Learning
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