AIMC Topic: Electroencephalography

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Motor Imagery Classification Based on Bilinear Sub-Manifold Learning of Symmetric Positive-Definite Matrices.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society Jul 7, 2016
In motor imagery brain-computer interfaces (BCIs), the symmetric positive-definite (SPD) covariance matrices of electroencephalogram (EEG) signals carry important discriminative information. In this paper, we intend to classify motor imagery EEG sign...
Movement Evoked Potentials, Motor Imagination Machine Learning Algorithms Reproducibility of Results Sensitivity and Specificity Linear Models Pattern Recognition, Automated Brain-Computer Interfaces Brain Mapping Electroencephalography Humans
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Assessing the user experience of older adults using a neural network trained to recognize emotions from brain signals.

Journal of biomedical informatics Jul 5, 2016
The use of Ambient Assisted Living (AAL) technologies as a means to cope with problems that arise due to an increasing and aging population is becoming usual. AAL technologies are used to prevent, cure and improve the wellness and health conditions o...
Humans Neural Networks, Computer Emotions Brain Electroencephalography Assisted Living Facilities Aged Aged, 80 and over Aging
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Spectral feature extraction of EEG signals and pattern recognition during mental tasks of 2-D cursor movements for BCI using SVM and ANN.

Australasian physical & engineering sciences in medicine Jul 4, 2016
Brain computer interface (BCI) is a new communication way between man and machine. It identifies mental task patterns stored in electroencephalogram (EEG). So, it extracts brain electrical activities recorded by EEG and transforms them machine contro...
Neural Networks, Computer Machine Learning Female Adult Humans Electroencephalography Signal Processing, Computer-Assisted Pattern Recognition, Automated Brain-Computer Interfaces Task Performance and Analysis Male Support Vector Machine Principal Component Analysis Least-Squares Analysis
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Real-time multi-channel monitoring of burst-suppression using neural network technology during pediatric status epilepticus treatment.

Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology Jun 11, 2016
OBJECTIVE: To develop a real-time monitoring system that has the potential to guide the titration of anesthetic agents in the treatment of pediatric status epilepticus (SE).
Brain Intensive Care Units Female Sensitivity and Specificity Humans Neural Networks, Computer Monitoring, Physiologic Electroencephalography Status Epilepticus Male Adolescent Child, Preschool Child
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Classification of Motor Imagery EEG Signals with Support Vector Machines and Particle Swarm Optimization.

Computational and mathematical methods in medicine May 30, 2016
Support vector machines are powerful tools used to solve the small sample and nonlinear classification problems, but their ultimate classification performance depends heavily upon the selection of appropriate kernel and penalty parameters. In this st...
Imagination Principal Component Analysis Electroencephalography Signal Processing, Computer-Assisted Hand Adult Humans Healthy Volunteers Signal Transduction Algorithms Foot Support Vector Machine Computer Simulation Motor Skills Models, Statistical Reproducibility of Results Movement
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Validation of a novel classification model of psychogenic nonepileptic seizures by video-EEG analysis and a machine learning approach.

Epilepsy & behavior : E&B May 20, 2016
The aim of this study was to validate a novel classification for the diagnosis of PNESs. Fifty-five PNES video-EEG recordings were retrospectively analyzed by four epileptologists and one psychiatrist in a blind manner and classified into four distin...
Humans Neural Networks, Computer Retrospective Studies Female Male Machine Learning Psychiatry Middle Aged Seizures Diagnosis, Differential Video Recording Single-Blind Method Focus Groups Electroencephalography Somatoform Disorders
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Deep Learning Representation from Electroencephalography of Early-Stage Creutzfeldt-Jakob Disease and Features for Differentiation from Rapidly Progressive Dementia.

International journal of neural systems May 3, 2016
A novel technique of quantitative EEG for differentiating patients with early-stage Creutzfeldt-Jakob disease (CJD) from other forms of rapidly progressive dementia (RPD) is proposed. The discrimination is based on the extraction of suitable features...
Wavelet Analysis Creutzfeldt-Jakob Syndrome Support Vector Machine Diagnosis, Differential Electroencephalography Humans Alzheimer Disease Brain Sensitivity and Specificity Retrospective Studies Male Female Middle Aged Aged Disease Progression
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Pattern Classification of Instantaneous Cognitive Task-load Through GMM Clustering, Laplacian Eigenmap, and Ensemble SVMs.

IEEE/ACM transactions on computational biology and bioinformatics May 3, 2016
The identification of the temporal variations in human operator cognitive task-load (CTL) is crucial for preventing possible accidents in human-machine collaborative systems. Recent literature has shown that the change of discrete CTL level during hu...
Pattern Recognition, Automated Task Performance and Analysis Psychophysics Support Vector Machine Cluster Analysis Electroencephalography Workload Humans Machine Learning Software Algorithms Male Computational Biology Cognition Young Adult Adult
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Mapping dynamical properties of cortical microcircuits using robotized TMS and EEG: Towards functional cytoarchitectonics.

NeuroImage May 3, 2016
Brain dynamics at rest depend on the large-scale interactions between oscillating cortical microcircuits arranged into macrocolumns. Cytoarchitectonic studies have shown that the structure of those microcircuits differs between cortical regions, but ...
Transcranial Magnetic Stimulation Neocortex Female Adult Male Brain Mapping Reference Values Nerve Net Evoked Potentials Robotics Electroencephalography Humans
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Extensions to a manifold learning framework for time-series analysis on dynamic manifolds in bioelectric signals.

Physical review. E Apr 29, 2016
This paper addresses the challenge of extracting meaningful information from measured bioelectric signals generated by complex, large scale physiological systems such as the brain or the heart. We focus on a combination of the well-known Laplacian ei...
Time Factors Heart Humans Electrocardiography Nonlinear Dynamics Machine Learning Electroencephalography Signal-To-Noise Ratio Brain Signal Processing, Computer-Assisted Electrophysiological Phenomena
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