Design of an EEG Acquisition System for Embedded Edge Computing

Design of an EEG Acquisition System for Embedded Edge Computing

Volume 5, Issue 4, Page No 119-129, 2020

Author’s Name: Kanishk Rai1,a), Keshav Kumar Thakur1, Preethi K Mane1, Narayan Panigrahi2

View Affiliations

1Department of Electronics and Instrumentation, BMS College of Engineering, 560019, India
2Centre for Artificial Intelligence and Robotics, DRDO, 560093, India

a)Author to whom correspondence should be addressed. E-mail: kr2252@gmail.com

Adv. Sci. Technol. Eng. Syst. J. 5(4), 119-129 (2020); a  DOI: 10.25046/aj050416

Keywords: Electroencephalogram (EEG), Internet of Things (IOT), Brain Computer Interface (BCI), Acquisition system, Edge Computing

Share
538 Downloads

Export Citations

Abstract

The human brain is one of the most complex machines on the planet. Being the only method to get real-time data with high temporal resolution from the brain makes EEG a highly sought upon signal in the neurological and psychiatric domain. However, recent developments in this field have made EEG more than just a tool for medical professionals. The decreasing size and increasing complexity of EEG acquisition systems have brought it out of the lab and into the field where it is used for varied applications like neurofeedback, person recognition and other recreational activities. Amalgamation of the EEG signal with new developing standards of Industry 4.0 to control basic IOT devices using edge computing techniques marks the next step in the design and development our low-cost yet robust Brain Computer Interface (BCI); which is just one of the many applications that a versatile and well-built EEG acquisition system can be used for.

Received: 17 April 2020, Accepted: 17 June 2020, Published Online: 12 July 2020

References (38)

  1. K. Rai et al., “Designing Low Cost Yet Robust EEG Acquisition System” in 2019 IEEE International Symposium on Smart Electronic Systems (iSES) (Formerly iNiS), Rourkela, India, 2019 https://doi.org/10.1109/iSES47678.2019.00096
  2. K.D. Nielsen, A.F. Cabrera, O.F. do Nascimento, “EEG based Brain Computer Interface – towards a better control Brain computer interface research at Aalborg university,” IEEE Transactions on Neural Systems and Rehabilitation Engineering., 14(2), Article ID 1642769, 202–204, 2006 https://doi.org/10.1109/TNSRE.2006.875529
  3. A. Caplier, S. Charbonnier, A. Picot, “On-Line Detection of Drowsiness using Brain and Visual Information,” IEEE Trans. Syst., Man, Cybern. A, Syst., Humans, 42(3), 773-774, May 2012 https://doi.org/10.1109/TSMCA.2011.2164242
  4. S. Roy, A. De, N. Panigrahi, “Saccade and Fix Detection from EOG Signal” in 2019 IEEE International Symposium on Smart Electronic Systems (iSES) (Formerly iNiS), Rourkela, India, 2019 https://doi.org/10.1109/iSES47678.2019.00099
  5. N. Panigrahi, A. De, S. Roy, “A Method to Detect Blink from The EEG Signal” in Intelligent Computing and Communication, ICICC 2019, Advances in Intelligent Systems and Computing, 1034, Springer, Singapore, 2019. https://doi.org/10.1007/978-981-15-1084-7_24
  6. N. Panigrahi, K. Lavu, S.K. Gorijala, P. Corcoran, S.P. Mohanty, “A Method for Localizing the Eye Pupil for Point-of-Gaze Estimation” in IEEE Potentials 38(1), 37-42, 2019. https://doi.org/10.1109/MPOT.2018.2850540
  7. J. Gomez-Gil, I. San-Jose-Gonzalez, L.F. Nicolas-Alonso, S. Alonso-Garcia “Steering a Tractor by Means of an EMG-Based Human-Machine Interface”, MDPI Sensors, 1424-8220, 2011. https://doi.org/10.3390/s110707110
  8. Y. Lin, Yijun Wang and Tzyy-Ping Jung “Assessing the feasibility of online SSVEP decoding in human walking using a consumer EEG headset”, Journal of NeuroEngineering and Rehabilitation, Published online 2014 Aug 9 https://doi.org/10.1186/1743-0003-11-119
  9. G. Rosas-Cholula, J.M. Ramirez-Cortes, V. Alarcon-Aquino, P. Gomez-Gil, J.J. Rangel-Magdaleno, C. Reyes-Garcia, “Gyroscope-Driven Mouse Pointer with an EMOTIV® EEG Headset and Data Analysis Based on Empirical Mode Decomposition” Sensors, 13, 10561-10583, 2013. https://doi.org/10.3390/s130810561
  10. T. A. Mariya Celin & B. Preethi “Brain Controlled and Switch Controlled Robotic Leg For The Paraplegics” ITSI Transactions on Electrical and Electronics Engineering (ITSI-TEEE), 2320 – 8945, Volume -1, Issue -1, 2013.
  11. H.A. Shedeed et al., “Brain EEG Signal Proccessing for Controlling a Robotic Arm”, IEEE, 152-157, 2013. https://doi.org/10.1109/ICCES.2013.6707191
  12. H. Kamlesh et al., “Brainwave Controlled Robot” International Research Journal of Engineering and Technology (IRJET), 02, 609-612, July 2015
  13. S. Chuan, Wu Chaozhong, Chu Duanfeng, Tian Fei, “A Design of Brain-computer Interface Control Platform for Intelligent Vehicle”, The 3rd International Conference on Transportation Information and Safety, June 25 – June 28, 2015. https://doi.org/10.1109/ICTIS.2015.7232147
  14. B.J.A. Rani, A. Umamakeswari “Electroencephalogram-based Brain Controlled Robotic Wheelchair” Indian Journal of Science and Technology, Vol 8(S9), 188–197, May 2015. https://dx.doi.org/10.17485/ijst/2015/v8iS9/65580
  15. E. Mathe and E. Spyrou, “Connecting a consumer brain-computer interface to an internet-of-things ecosystem,” in Proceedings of the 9th ACM International Conference on PErvasive Technologies Related to Assistive Environments, ser. PETRA ’16, 90:1–90:2, 2016. https://doi.org/10.1145/2910674.2935844
  16. C. P. Brennan, P. J. McCullagh, L. Galway, and G. Lightbody, “Promoting autonomy in a smart home environment with a smarter interface” in Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2015. https://doi.org/10.1109/EMBC.2015.7319522
  17. B. Sujatha, G. Ambica “Eeg Based Brain Computer Interface For Controlling Home Appliances” International Research Journal of Engineering and Technology (IRJET) Volume: 02 Issue: 09, Dec-2015.
  18. N. Abdel Ilah et al., “EEG-based Brain-computer Interface for Automating Home Appliances” JOURNAL OF COMPUTERS, 9, NO. 9, SEPTEMBER 2014. http://dx.doi.org/10.4304/jcp.9.9.2159-2166
  19. K.E. Anu et al., “Non Invasive Electroencephalograph Control for Smart Home Automation” International Journal of Advanced Research in Electrical, Electronics and Instrumentation Engineering 5, Special Issue 3, March 2016.
  20. K. Alhalaseh et al., “Home Automation Application Using Eeg Sensor” Proceedings of 98th The IRES International Conference, Antalya, Turkey, 21st-22nd January, 2018.
  21. H. Mohammad et al., “Automated Classification of L/R Hand Movement EEG Signals using Advanced Feature Extraction and Machine Learning” (IJACSA) International Journal of Advanced Computer Science and Applications, 4, No. 6, 2013. http://dx.doi.org/10.14569/IJACSA.2013.0406
  22. T. Uktveris, V. Jusas “Development of a Modular Board for EEG Signal Acquisition” MDPI Sensors, Published: 3 July 2018. https://doi.org/10.3390/s18072140
  23. B. Luan, M. Sun, W. Jia “Portable Amplifier Design for a Novel EEG Monitor in Point-of- Care Applications” Proc IEEE Annu Northeast Bioeng Conference, 2014. https://doi.org/10.1109/NEBC.2012.6207127
  24. A.J. Bhagawati, R. Chutia “Design of Single Channel Portable Eeg Signal Acquisition System For Brain Computer Interface Application” International Journal of Biomedical Engineering and Science (IJBES), 3(1), January 2016. http://dx.doi.org/10.5121/ijbes.2016.310
  25. C. Jaganathan, A. Amudhavalli, T. Janani, M. Dhanalakshmi, Nirmala Madian “Automated Algorithm for Extracting EEG Of A Human Eeg” International Journal of Science, Engineering and Technology Research (IJSETR) 4(4), April 2015.
  26. J. Tian, W. Song “LabVIEW for EEG Signal Processing” Saudi Journal of Engineering and Technology 1(4), Oct-Dec, 2016. DOI: https://doi.org/10.21276/sjeat.2016.1.4.10
  27. M. Rajya Lakshmi, Dr. T. V. Prasad, Dr. V. Chandra Prakash “Survey on EEG Signal Processing Methods” International Journal of Advanced Research in Computer Science and Software Engineering 4(1), 84-91, January 2014.
  28. N.R. Miss et al., “Review: Wavelet transform based electroencephalogram methods” International Journal of Trend in Scientific Research and Development (IJTSRD) 2(3), Mar-Apr Page: 1777, 2018.
  29. T. Pham et al., “A Study on the Feasibility of Using EEG Signals for Authentication Purpose” ICONIP 2013, Part II, LNCS 8227, 562–569, 2013.
  30. T. Wilaiprasitporn, A. Ditthapron, K. Matchaparn, T. Tongbuasirilai, N. Banluesombatkul, E. Chuangsuwanich “Affective EEG-Based Person Identification Using the Deep Learning Approach” Journal of Latex Class Files, 14, August 2018. https://doi.org/10.1109/TCDS.2019.2924648
  31. M. Byoung-Kyong, H. Suk, A. Min-Hee, L. Min-Ho, L. Seong-Whan, “Individual Identification Using Cognitive Electroencephalographic Neurodynamics” IEEE Transactions on Information Forensics And Security, 12(9), September 2017. https://doi.org/10.1109/TIFS.2017.2699944
  32. G. Matthews, “Metrics for individual differences in EEG response to cognitive workload: Optimizing performance prediction” Personality and Individual Differences 118, 22–28, 2017 https://doi.org/10.1016/j.paid.2017.03.002
  33. D. Gajic, Z. Djurovic, S.D. Gennaro, F. Gustafsson “Classification of EEG Signals for Detection of Epileptic Seizures Based on Wavelets and Statistical Pattern Recognition” Biomedical Engineering: Applications, Basis and Communications, 26(21), 2014. https://doi.org/10.4015/S1016237214500215
  34. S. Mantri, V. Patil, R. Mitkar “EEG Based Emotional Distress Analysis – A Survey” International Journal of Engineering Research and Development 4(6), 24-28, 2012.
  35. F. Al-shargie, T.B. Tang, N. Badruddin, M. Kiguchi “Mental Stress Quantification Using Eeg Signals” ResearchGate Publication 2016, from book International Conference for Innovation in Biomedical Engineering and Life Sciences: ICIBEL2015, Putrajaya, Malaysia, (15-19), 6-8 2015. https://doi.org/10.1007/978-981-10-0266-3_4
  36. R. Khosrowabadi “Stress and Perception of Emotional Stimuli: Long-term Stress Rewiring the Brain” Basic and Clinical Neuroscience, 9(2), 107-120, 2017. https://dx.doi.org/10.29252%2FNIRP.BCN.9.2.107
  37. M. Jeffrey et al., “Acute EEG Patterns Associated with Transient Ischemic Attack” Clinical EEG and Neuroscience, Article first published online; 2019. https://doi.org/10.1177/1550059418790708
  38. M. Nami, S. Mehrabi, S. Derman “Employing Neural Network Methods to Label Sleep EEG Micro-Arousals in Obstructive Sleep Apnea Syndrome” Journal of Advanced Medical Sciences and Applied Technologies 3(4):221-226, 2017. http://dx.doi.org/10.32598/jamsat.3.4.221

Cited By

Citations by Dimensions

Citations by PlumX

Google Scholar

(Click Here)

Scopus

(Click Here)

Metrics