Brain-Machine Interface System Supporting Subjects with Cognitive Impairments via EEG Signals

Authors

  • S. Srinivasan Professor, Department of Biomedical Engineering, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Saveetha University, Saveetha Nagar, Thandalam, Chennai-602105
  • K. Vimalanathan Assistant Professor, Department of Industrial Engineering, College of Engineering Guindy, Anna University, Chennai. 600025
  • Marxim Rahula Bharathi. B Associate Professor, Aditya College of Engineering, Surampalem, Andhra Pradesh
  • J. S. Christy Mano Raj Professor, Department of Electrical and Electronics Engineering, Government College of Engineering, Bargur
  • K. B. Kishore Mohan Associate Professor, Department of Bio Medical Engineering, Saveetha, Engineering College, Chennai

Keywords:

Brain-Machine Interface, EEG, emotion recognition, feature extraction

Abstract

A cognitive dysfunction can result from a multitude of factors, which can influence one's physical and mental fulfillment. It is therefore challenging for a human or animal to continue out their daily routines when they encounter issues with their upper or lower limbs due to impairment in the brain. Therefore in this article, thorough clarification about this for stimulating the brain signals is offered. The method called brain-machine interface (BMI) is proposed which uses EEG signals for utilizing brain impulses to develop an avenue for communication for individuals who are unfit to talk or paralyzed. We supply an innovative technique for recognizing emotions focused on the generic mixture dispersion framework to identify the emotion appearances by an immobile subject. The main advantage of this simulation is its imbalanced dispersion, which aids in the symmetric or asymmetric style of EEG signal gathering. The considerable amount of signal fluctuation in the EEG makes the recommended approach especially appropriate for precisely recognizing feelings. Happy, sad, neutral, and boredom are the basic feelings taken into consideration in this investigation, and a mean emotion recognition accuracy rate of 89% is obtained.

Downloads

Download data is not yet available.

References

Hwang, H. J., Kim, S., Choi, S., & Im, C. H. (2013). EEG-based brain-computer interfaces: a thorough literature survey. International Journal of Human-Computer Interaction, 29(12), 814-826.

Liu, X., Zhang, M., Richardson, A. G., Lucas, T. H., & Van der Spiegel, J. (2016). Design of a closed-loop, bidirectional brain machine interface system with energy efficient neural feature extraction and PID control. IEEE transactions on biomedical circuits and systems, 11(4), 729-742.

Moradi, E., Amendola, S., Björninen, T., Sydänheimo, L., Carmena, J. M., Rabaey, J. M., & Ukkonen, L. (2014). Backscattering neural tags for wireless brain-machine interface systems. IEEE Transactions on Antennas and Propagation, 63(2), 719-726.

Wagh, K. P., & Vasanth, K. (2019). Electroencephalograph (EEG) based emotion recognition system: A review. Innovations in Electronics and Communication Engineering: Proceedings of the 6th ICIECE 2017, 37-59.

Katona, J., & Kovari, A. (2015). EEG-based Computer Control Interface for Brain-Machine Interaction. International Journal of Online Engineering, 11(6).

Rak, R. J., Kołodziej, M., & Majkowski, A. (2012). Brain-computer interface as measurement and control system the review paper. Metrology and Measurement Systems, 427-444.

Bird, J. J., Ekart, A., Buckingham, C. D., & Faria, D. R. (2019, April). Mental emotional sentiment classification with an eeg-based brain-machine interface. In Proceedings of theInternational Conference on Digital Image and Signal Processing (DISP’19).

Carella, T., De Silvestri, M., Finedore, M., Haniff, I., & Esmailbeigi, H. (2018, July). Emotion recognition for brain machine interface: non-linear spectral analysis of EEG signals using empirical mode decomposition. In 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) (pp. 223-226). IEEE.

Uma, M., & Sridhar, S. S. (2013, August). A feasibility study for developing an emotional control system through brain computer interface. In 2013 international conference on human computer interactions (ICHCI) (pp. 1-6). IEEE.

Sargent, G., Zhang, H., Morgan, A., Van Camp, A., D'Arcy, A., Cassedy, A., ... & Asari, V. (2017, November). Brain machine interface for useful human interaction via extreme learning machine and state machine design. In 2017 IEEE Symposium Series on Computational Intelligence (SSCI) (pp. 1-5). IEEE.

Chen, X., Tao, X., Wang, F. L., & Xie, H. (2022). Global research on artificial intelligence-enhanced human electroencephalogram analysis. Neural Computing and Applications, 1-39.

Atkinson, J., & Campos, D. (2016). Improving BCI-based emotion recognition by combining EEG feature selection and kernel classifiers. Expert Systems with Applications, 47, 35-41.

Krishna, N. M., Sekaran, K., Vamsi, A. V. N., Ghantasala, G. P., Chandana, P., Kadry, S., ... & Damaševičius, R. (2019). An efficient mixture model approach in brain-machine interface systems for extracting the psychological status of mentally impaired persons using EEG signals. Ieee Access, 7, 77905-77914.

Bano, K. S., Bhuyan, P., & Ray, A. (2022, December). EEG-Based Brain Computer Interface for Emotion Recognition. In 2022 5th International Conference on Computational Intelligence and Networks (CINE) (pp. 1-6). IEEE.

Jaison, F., & Bhardwaj, S. (2023). EEG-Based brain-machine interface for categorizing cognitive sentimental emotions. Multidisciplinary Science Journal, 5.

Downloads

Published

07.02.2024

How to Cite

Srinivasan, S. ., Vimalanathan, K. ., Bharathi. B, M. R. ., Mano Raj, J. S. C. ., & Mohan, K. B. K. . (2024). Brain-Machine Interface System Supporting Subjects with Cognitive Impairments via EEG Signals. International Journal of Intelligent Systems and Applications in Engineering, 12(15s), 151–157. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/4728

Issue

Vol. 12 No. 15s (2024)

Section

Research Article

License

Creative Commons License

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

All papers should be submitted electronically. All submitted manuscripts must be original work that is not under submission at another journal or under consideration for publication in another form, such as a monograph or chapter of a book. Authors of submitted papers are obligated not to submit their paper for publication elsewhere until an editorial decision is rendered on their submission. Further, authors of accepted papers are prohibited from publishing the results in other publications that appear before the paper is published in the Journal unless they receive approval for doing so from the Editor-In-Chief.

IJISAE open access articles are licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. This license lets the audience to give appropriate credit, provide a link to the license, and indicate if changes were made and if they remix, transform, or build upon the material, they must distribute contributions under the same license as the original.

Most read articles by the same author(s)