BCI-Based Smart Room Control using EEG Signals

Authors

  • Oger Zaya Amanuel Department of Mechatronics Engineering/ Al-Khwarizmi College of Engineering/ University of Baghdad
  • Yarub Alazzawi Department of Mechatronics Engineering/ Al-Khwarizmi College of Engineering/ University of Baghdad

DOI:

https://doi.org/10.22153/kej.2022.09.004

Abstract

In this paper, we implement and examine a Simulink model with electroencephalography (EEG) to control many actuators based on brain waves. This will be in great demand since it will be useful for certain individuals who are unable to access some control units that need direct contact with humans. In the beginning, ten volunteers of a wide range of (20-66) participated in this study, and the statistical measurements were first calculated for all eight channels. Then the number of channels was reduced by half according to the activation of brain regions within the utilized protocol and the processing time also decreased. Consequently, four of the participants (three males and one female) were chosen to examine the Simulink model during different actions. The model contained: input signals, data selection according to the activation regions in the brain, features extraction, classification according to the frequency ranges of each action, and an interface with an embedded system to control the actuators.

 

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References

S. Abdulkader, A. Atia and M. Mostafa, "Brain computer interfacing: Applications and challenges," Egyptian Informatics Journal, vol. 16, pp. 213--230, 2015.

R. Ramadan, S. Refat, M. Elshahed and R. Ali, Basics of brain computer interface, Springer, 2015, pp. 31--50.

R. Ramadan and A. Vasilakos, "Brain computer interface: control signals review," Neurocomputing, vol. 223, pp. 26--44, 2017.

F. Lotte, L. Bougrain and M. Clerc, "Electroencephalography (EEG)-based brain-computer interfaces," Wiley Encyclopedia of Electrical and Electronics Engineering, p. 44, 2015.

J. Wolpaw, N. Birbaumer, D. McFarland, G. Pfurtscheller and T. Vaughan, "Brain-computer interfaces for communication and control," Clinical neurophysiology, vol. 113, pp. 767--791, 2002.

P. Abhang, B. Gawali and S. Mehrotra, "Technological basics of EEG recording and operation of apparatus," Introduction to EEG-and Speech-Based Emotion Recognition, pp. 19--50, 2016.

V. Rohith, T. Prajitha and S. Suresh, "EEG Signal Analyzing and Simulation Under Computerized Technological Support," International Journal of Engineering and Technology (UAE), vol. 7, pp. 38--41, 2018.

P. Pelayo, H. Murthy and K. George, "Brain-computer interface controlled robotic arm to improve quality of life," in 2018 IEEE International Conference on Healthcare Informatics (ICHI), IEEE, 2018, pp. 398--399.

M. Y, K. Djouani and K. Anish, "A Matlab/Simulink framework for real time implementation of endogenous brain computer interfaces," in 2017 IEEE AFRICON, IEEE, 2017, pp. 100--105.

G. Phan, "Analysis and processing EMG signals using Simulink," Turkish Journal of Computer and Mathematics Education (TURCOMAT), vol. 12, pp. 7055--7060, 2021.

M. Menteş, S. Özbal and G. Ertaş, "Experiences on 3D Printing of an EEG Headset," in 2021 Medical Technologies Congress (TIPTEKNO), IEEE, 2021, pp. 1--4.

V. Shivappa, B. Luu, M. Solis and K. George, "Home automation system using brain computer interface paradigm based on auditory selection attention," in 2018 IEEE international instrumentation and measurement technology conference (I2MTC), IEEE, 2018, pp. 1--6.

A. Rajavenkatanarayanan, "Human factors analysis and monitoring to enhance human-robot collaboration," Ph.D. Dissertation, University of Texas at Arlington, 2021.

J. Moini and P. Piran, Functional and Clinical Neuroanatomy: A Guide for Health Care Professionals, Academic Press, 2020, pp. 177--190.

N. Al-Qazzaz, M. Sabir, S. Ali, S. Ahmad and K. Grammer, "Effective EEG Channels for emotion identification over the brain regions using differential evolution algorithm," in 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), IEEE, 2019, pp. 4703--4706.

S. Jaafer, "Hurst Exponent and Tsallis Entropy Markers for Epileptic Detection from Children," Al-Khwarizmi Engineering Journal, vol. 17, p. 34–42, 2021.

M. Asogbon, O. Samuel, X. Li, N. Jiang, O. Idowu, Y. Jiang, Y. Geng, A. Al-Timemy and G. Li, "A Robust Multi-Channel EEG Signals Preprocessing Method for Enhanced Upper Extremity Motor Imagery Decoding," in 2020 IEEE International Conference on Mechatronics and Automation (ICMA), 2020, pp. 1929-1934.

A. Almahdi, A. Yaseen and A. Dakhil, "EEG Signals Analysis for Epileptic Seizure Detection Using DWT Method with SVM and KNN Classifiers," Iraqi Journal of Science, no. 2, p. 54–62, 2021.

S. Valipour, A. Shaligram and G. Kulkarni, "Detection of an alpha rhythm of EEG signal based on EEGLAB," Int J Eng Res Appl, vol. 4, pp. 154--159, 2014.

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Published

2022-12-01

How to Cite

Amanuel , O. Z., & Alazzawi , Y. (2022). BCI-Based Smart Room Control using EEG Signals. Al-Khwarizmi Engineering Journal, 18(4), 60–72. https://doi.org/10.22153/kej.2022.09.004