Display options
Cite
Thilagaraj M, Dwarakanath B, Ramkumar S, et al. Eye Movement Signal Classification for Developing Human-Computer Interface Using Electrooculogram. J Healthc Eng. 2021;2021:7901310doi: 10.1155/2021/7901310.
Thilagaraj, M., Dwarakanath, B., Ramkumar, S., Karthikeyan, K., Prabhu, A., Saravanakumar, G., Rajasekaran, M. P., & Arunkumar, N. (2021). Eye Movement Signal Classification for Developing Human-Computer Interface Using Electrooculogram. Journal of healthcare engineering, 20217901310. https://doi.org/10.1155/2021/7901310
Thilagaraj, M, et al. "Eye Movement Signal Classification for Developing Human-Computer Interface Using Electrooculogram." Journal of healthcare engineering vol. 2021 (2021): 7901310. doi: https://doi.org/10.1155/2021/7901310
Thilagaraj M, Dwarakanath B, Ramkumar S, Karthikeyan K, Prabhu A, Saravanakumar G, Rajasekaran MP, Arunkumar N. Eye Movement Signal Classification for Developing Human-Computer Interface Using Electrooculogram. J Healthc Eng. 2021 Dec 08;2021:7901310. doi: 10.1155/2021/7901310. eCollection 2021. PMID: 34925741; PMCID: PMC8674061.
Copy Download .nbib Format: NLM
Share it on

J Healthc Eng. 2021 Dec 08;2021:7901310. doi: 10.1155/2021/7901310. eCollection 2021.

Eye Movement Signal Classification for Developing Human-Computer Interface Using Electrooculogram.

Journal of healthcare engineering

M Thilagaraj, B Dwarakanath, S Ramkumar, K Karthikeyan, A Prabhu, Gurusamy Saravanakumar, M Pallikonda Rajasekaran, N Arunkumar

Affiliations

  1. Department of Electronics and Instrumentation Engineering, Karpagam College of Engineering, Coimbatore, Tamil Nadu, India.
  2. Department of Information Technology, SRM Institute of Science and Technology, Chennai, Tamil Nadu, India.
  3. School of Computing, Kalasalingam Academy of Research and Education, Krishnankoil, Virudhunagar, Tamil Nadu, India.
  4. Department of Electrical and Electronics Engineering, Ramco Institute of Technology, Rajapalayam, Tamil Nadu, India.
  5. K. Ramakrishnan College of Engineering, Trichy, Tamil Nadu, India.
  6. Department of Electrical and Electronics Technology, Ethiopian Technical University, Addis Ababa, Ethiopia.
  7. Department of Electronics and Communication Engineering, Kalasalingam Academy of Research and Education, Krishnankoil, Virudhunagar, Tamil Nadu, India.
  8. Department of Biomedical Engineering, Rathinam Technical Campus, Coimbatore, Tamil Nadu, India.

PMID: 34925741 PMCID: PMC8674061 DOI: 10.1155/2021/7901310

Abstract

Human-computer interfaces (HCI) allow people to control electronic devices, such as computers, mouses, wheelchairs, and keyboards, by bypassing the biochannel without using motor nervous system signals. These signals permit communication between people and electronic-controllable devices. This communication is due to HCI, which facilitates lives of paralyzed patients who do not have any problems with their cognitive functioning. The major plan of this study is to test out the feasibility of nine states of HCI by using modern techniques to overcome the problem faced by the paralyzed. Analog Digital Instrument T26 with a five-electrode system was used in this method. Voluntarily twenty subjects participated in this study. The extracted signals were preprocessed by applying notch filter with a range of 50 Hz to remove the external interferences; the features were extracted by applying convolution theorem. Afterwards, extracted features were classified using Elman and distributed time delay neural network. Average classification accuracy with 90.82% and 90.56% was achieved using two network models. The accuracy of the classifier was analyzed by single-trial analysis and performances of the classifier were observed using bit transfer rate (BTR) for twenty subjects to check the feasibility of designing the HCI. The achieved results showed that the ERNN model has a greater potential to classify, identify, and recognize the EOG signal compared with distributed time delay network for most of the subjects. The control signal generated by classifiers was applied as control signals to navigate the assistive devices such as mouse, keyboard, and wheelchair activities for disabled people.

Copyright © 2021 M. Thilagaraj et al.

Conflict of interest statement

The authors declare no conflicts of interest.

Publication Types