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


  • 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


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


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.


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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



Research Article