V.G. Rajendran, S. Jayalalitha, K. Adalarasu, T. Nirmalraj
V.G. Rajendran1*, Dr. S. Jayalalitha2, Dr. K. Adalarasu3, T. Nirmalraj4
1Assistant Prof, Department of ECE, School of EEE, SASTRA Deemed to be University, SRC, Kumbakonam, Tamil Nadu, India.
2Associate Prof., Department of EIE, SASTRA Deemed to be University, Thanjavur, Tamil Nadu, India.
3Dean and Professor, Departments of EIE, School of EEE, SASTRA Deemed to be University, Thanjavur, Tamil Nadu, India.
4Assistant Prof, Department of ECE, School of EEE, SASTRA Deemed to be University, SRC, Kumbakonam, Tamil Nadu, India.
Volume - 14,
Issue - 9,
Year - 2021
Brain-Computer Interface (BCI) plays a major role in current technologies such as rehabilitation, control of devices, and various medical applications. BCI or brain-machine interface provides direct communication between a brain signal and an external device. In this paperwork, a detailed survey was carried out with the design of single-channel EEG system for various applications. Also, this paper mainly focused on the development of single-channel electroencephalography (EEG) signal acquisition system which includes a preamplifier, bandpass filter, post-amplifier and level shifter circuits. The design of the preamplifier and post-amplifier circuit was carried out by integrated circuits (IC) such as instrumentation amplifier IN128P and bandpass filter with the help of low power operational amplifier LM324. The developed single-channel acquisition board was tested by acquiring an electrooculogram (EOG) signal with closed and opened eye conditions. The acquired signal is displayed and stored in the computer with the help of the HBM-DAQ unit.
Cite this article:
V.G. Rajendran, S. Jayalalitha, K. Adalarasu, T. Nirmalraj. Development of single channel EEG Acquisition system for BCI applications. Research Journal of Pharmacy and Technology. 2021; 14(9):4705-9. doi: 10.52711/0974-360X.2021.00818
V.G. Rajendran, S. Jayalalitha, K. Adalarasu, T. Nirmalraj. Development of single channel EEG Acquisition system for BCI applications. Research Journal of Pharmacy and Technology. 2021; 14(9):4705-9. doi: 10.52711/0974-360X.2021.00818 Available on: https://rjptonline.org/AbstractView.aspx?PID=2021-14-9-32
1. Luis Fernando Nicolas-Alonso and Jaime Gomez-Gil. Brain Computer Interfaces, a Review. Sensors. 2012; 12: 1211-1279.
2. Tejinder Kaur, Birinder Singh. Brain Computer Interface: A Review. International Research Journal of Engineering and Technology. 2017; 4(4): 3594-3601.
3. Prashant P, Joshi A and Gandhi V. Brain computer interface: A review. 5th Nirma University International Conference on Engineering (NUiCONE). 2015; 1-5.
4. Ravikumar D, Devi V, Arun Raaza. Development of Brain Computer Interface, using Neural Network. Research J. Pharm. and Tech. 2018; 11(10): 4397-4400.
5. Koudelkova Z., Strmiska M. Introduction to the identification of brain waves based on their frequency. MATEC Web of Conferences. 210, 2018.
6. Anupama. H. S,, N.K. Cauvery, Lingaraju. G.M. Brain Computer Interface and its types – A study. International Journal of Advances in Engineering and Technology. 2012; 3(2): 739-745.
7. Amlan Jyoti Bhagawati, Riku Chutia. Design of single channel portable EEG signal acquisition system for brain computer application. International Journal of Biomedical Engineering and Science (IJBES). 2016; 3(1).
8. Minglong Y, Qingsong A and Quan L. Design of a high-performance EEG acquisition system for unshielded environment. Proceedings of 2012 IEEE/ASME 8th IEEE/ASME International Conference on Mechatronic and Embedded Systems and Applications. 2012: 202-206.
9. Chen X and Wang Z. J. Design and Implementation of a Wearable, Wireless EEG Recording System. 5th International Conference on Bioinformatics and Biomedical Engineering. 2011: 1-4.
10. Zhu. L, Chen. H, and Zhang. X. Design of portable multichannel EEG signal acquisition system. International Conference on Biomedical Engineering and Informatics. 2009:1–4.
11. Dias. NS, Carmo. JP, Mendes. PM, and Correia. JH. Wireless instrumentation system based on dry electrodes for acquiring EEG signals. Med Eng Phys. 2011; 34(7): 972-81.
12. Uktveris T, Jusas V. Development of a Modular Board for EEG Signal Acquisition. Sensors, 2018:18.
13. Ravikumar D, Devi V, Arun Raaza. Development of Brain Computer Interface, using Neural Network. Research J. Pharm. and Tech 2018; 11(10): 4397-4400.
14. Keshava Murthy G.N., Zaved Ahmed Khan. Cognitive attention behaviour detection systems using Electroencephalograph (EEG) signals. Research J. Pharm. and Tech, 2014; 7(2): 238-247.
15. Lazarou Ioulietta, Nikolopoulos Spiros, Petrantonakis Panagiotis C., Kompatsiaris Ioannis, Tsolaki Magda. EEG-Based Brain–Computer Interfaces for Communication and Rehabilitation of People with Motor Impairment: A Novel Approach of the 21st Century. Frontiers in Human Neuroscience, vol.12, 2018.
16. Ganesan P, Sathish. BS.. Murugesan. A Simple Approach to Automated Brain Tumor Segmentation and Classification. Research J. Pharm. and Tech. 2019; 12(7): 3564-3568.
17. Grace Kanmani Prince P, Rani Hemamalini, Anitha U, Premalatha J, Sudheera K. Detection of seizure using EEG Signals by Supervised Learning Algorithms. Research J. Pharm. and Tech. 2017; 10(10): 3443-3448.
18. Harikumar Rajaguru, Sunil Kumar Prabhakar. Wavelet Neural Networks, Elman Backpropagation and Multilayer Perceptrons for Epilepsy Classification from EEG Signals. Research J. Pharm. and Tech. 2018; 11(4):1301-1306.