Home
  • English
  • ÄŒeÅ¡tina
  • Deutsch
  • Español
  • Français
  • Gàidhlig
  • LatvieÅ¡u
  • Magyar
  • Nederlands
  • Português
  • Português do Brasil
  • Suomi
  • Log In
    New user? Click here to register. Have you forgotten your password?
Home
  • Browse Our Collections
  • Publications
  • Researchers
  • Research Data
  • Institutions
  • Statistics
    • English
    • ÄŒeÅ¡tina
    • Deutsch
    • Español
    • Français
    • Gàidhlig
    • LatvieÅ¡u
    • Magyar
    • Nederlands
    • Português
    • Português do Brasil
    • Suomi
    • Log In
      New user? Click here to register. Have you forgotten your password?
  1. Home
  2. Research Output and Publications
  3. Faculty of Electronic Engineering & Technology (FKTEN)
  4. Journal Articles
  5. Intelligent robot chair with communication aid using TEP responses and higher order spectra band features
 
Options

Intelligent robot chair with communication aid using TEP responses and higher order spectra band features

Journal
Informatics
ISSN
2617-6963
1816-0301
Date Issued
2021
Author(s)
Sathees Kumar Nataraj
University Bahrain, Salmabad
Paulraj Murugesa Pandiyan
Sri Ramakrishna Institute of Technology, India
Sazali Bin Yaacob
UniKL Malaysian Spanish Institute
Abdul Hamid Adom
Universiti Malaysia Perlis
DOI
10.37661/1816-0301-2020-17-4-92-103
Abstract
In recent years, electroencephalography-based navigation and communication systems for differentially enabled communities have been progressively receiving more attention. To provide a navigation system with a communication aid, a customized protocol using thought evoked potentials has been proposed in this research work to aid the differentially enabled communities. This study presents the higher order spectra based features to categorize seven basic tasks that include Forward, Left, Right, Yes, NO, Help and Relax; that can be used for navigating a robot chair and also for communications using an oddball paradigm. The proposed system records the eight-channel wireless electroencephalography signal from ten subjects while the subject was perceiving seven different tasks. The recorded brain wave signals are pre-processed to remove the interference waveforms and segmented into six frequency band signals, i. e. Delta, Theta, Alpha, Beta, Gamma 1-1 and Gamma 2. The frequency band signals are segmented into frame samples of equal length and are used to extract the features using bispectrum estimation. Further, statistical features such as the average value of bispectral magnitude and entropy using the bispectrum field are extracted and formed as a feature set. The extracted feature sets are tenfold cross validated using multilayer neural network classifier. From the results, it is observed that the entropy of bispectral magnitude feature based classifier model has the maximum classification accuracy of 84.71 % and the value of the bispectral magnitude feature based classifier model has the minimum classification accuracy of 68.52 %.
Subjects
  • Intelligent robot cha...

  • Thought evoked potent...

  • Bispectrum estimation...

  • Multilayer neural net...

File(s)
research repository notification.pdf (4.4 MB)
google-scholar
Views
Downloads
  • About Us
  • Contact Us
  • Policies