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A New Approach on HCI Extracting Conscious Jaw Movements Based on EEG Signals Using Machine Learnings.

Machine computer interfaces (MCI) are assistive technologies enabling paralyzed peoples to control and communicate their environments. This study aims to discover and represents a new approach on MCI using left/right motions of voluntary jaw movements stored in electroencephalogram (EEG). It extract...

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Published in:Journal of Medical Systems Vol. 42; no. 9; pp. 1 - 2
Main Author: Bascil, M. Serdar
Format: equations & formulas pictorial research tables/charts Journal Article
Published: Springer Nature Sep2018
Online Access:View this record in EBSCOhost
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      dt: Sep2018
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      pub: Springer Nature
      place: New York, New York
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        10.1007/s10916-018-1027-1
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        atl: A New Approach on HCI Extracting Conscious Jaw Movements Based on EEG Signals Using Machine Learnings.
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        au: Bascil, M. Serdar
        affil: Department of Electrical and Electronics Engineering, Bozok University, 66200, Yozgat, Turkey
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        subj:
          Jaw Physiology
          Electroencephalography
          Machine Learning
          Assistive Technology
          Human
          User-Computer Interface
          Quadriplegia
          Female
          Male
          Adult
          Factor Analysis
          Experimental Studies
          Adult: 19-44 years
          Female
          Male
      ab: Machine computer interfaces (MCI) are assistive technologies enabling paralyzed peoples to control and communicate their environments. This study aims to discover and represents a new approach on MCI using left/right motions of voluntary jaw movements stored in electroencephalogram (EEG). It extracts brain electrical activities on EEG produced by voluntary jaw movements and converts these activities to machine control commands. Jaw-operated machine computer interface is a new way of MCI entitled as jaw machine interface (JMI) provides new functionality for paralyzed people to assist available environmental devices using their jaw motions. In this article, root mean square (RMS) and standard deviation (STD) features of signals are extracted and hemispherical pattern changes are computed and compared as offline analysis approach. A statistical algorithm, principle component analysis (PCA), is used to reduce high dimensional data and two types of machine learning algorithms which are linear discriminant analysis (LDA) and support vector machine (SVM) incorporating k-fold cross validation technique are employed to identify pattern changes by utilizing the features of horizontal jaw movements stored in EEG.
      pubtype: Academic Journal
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        equations & formulas
        pictorial
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        Journal Article
      ougenre: Article
    language: English
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