Advertisement

Brain–Computer Interface-Based Fear Detection: A Self-defense Mechanism

  • Rheya Chakraborty
  • Arup Kumar Chattopadhyay
  • Animesh Kairi
  • Mohuya Chakraborty
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 811)

Abstract

In this paper, brain–computer interface (BCI)-based fear signal detection and subsequent self-defense system has been presented. Self-defense is a countermeasure that involves protecting the health and well-being of oneself from detriment by others including human beings, animals. The system aims at designing an automated alert mechanism that operates involuntarily by taking into consideration the biological signals of a human being without the knowledge of the victim. This device is known as Silent Alert Self-Defense System (SiLERT). It is a small device that may be embedded in a cap, which monitors the human heartbeat rate and brainwaves to detect the fearful condition of a person when he is in danger. Upon detection of fear signals, the system automatically dials and sends emergency alert information including the location of the user via GPS to some predefined mobile numbers silently without the knowledge of the victim and attacker for help. The system has been designed and implemented using heartbeat and brain sensors along with a microcontroller to do the necessary steps. Real-time experimental results for two cases performed on two persons show the normal as well as fear state of mind. The GSM module attached to the system which automatically sends alert to the predefined mobile numbers is clearly shown experimentally.

Keywords

Brain–computer interface Brainwaves Fear signal Heartbeat Automatic Involuntary Self-defense Silent alert 

References

  1. 1.
    Krucoff, M.O., Rahimpour, S., Slutzky, M.W., Edgerton, V.R., Turner, D.A.: Enhancing nervous system recovery through neurobiologics, neural interface training, and neurorehabilitation. Front. Neuroprosthetics 10, 584 (2016); J. 2(5), 99–110.  https://doi.org/10.3389/fnins.2016.00584.5186786.pmid28082858.author
  2. 2.
    Donchin, E., Spencer, K.M., Wijesinghe, R.: The mental prosthesis: assessing the speed of a P300-Based brain-computer interface. IEEE Trans. Rehabil. Eng. 8(2), 174–179 (2000)CrossRefGoogle Scholar
  3. 3.
    Bozinovski, S., Sestakov, M., Bozinovska, L.: Using EEG alpha rhythm to control a mobile robot. In: Proceedings of IEEE Annual Conference of Medical and Biological Society, pp. 1515–1516, New Orleans (1988)Google Scholar
  4. 4.
    Leuthardt1, E.C., Schalk, G., Wolpaw, J.R., Ojemann, J.G., Moran, D.W.: A brain–computer interface using electrocorticographic signals in humans. J. Neural Eng. 63–71 (2004). stacks.iop.org/JNE/1/63,  https://doi.org/10.1088/1741-2560/1/2/001CrossRefGoogle Scholar
  5. 5.
    Lazar, S.W., Bush, G., Gollub, R.L., Fricchione, G.L., Khalsa, G., Benson, H.: Functional brain mapping of the relaxation response and meditation. Neuroreport (2000)Google Scholar
  6. 6.
    Dorfer, C., Widjaja, E., Ochi, A., Carter, O.S.I., Rutka, J.T.: Epilepsy surgery: recent advances in brain mapping, neuroimaging and surgical procedures. J. Neurosurg. Sci. 59(2), 141–155 (2015)Google Scholar
  7. 7.
    Sriranjini, R.: GPS and GSM based self defense system for women safety. J. Electr. Electron. Syst. (2017).  https://doi.org/10.4172/2332-0796.1000233
  8. 8.
    Johnson, N.N., Carey, J., Edelman, B.J., Doud, A., Grande, A., Lakshminarayan, K., He, B.: Combined rTMS and virtual reality brain-computer interface training for motor recovery after stroke. J. Neural Eng. 15(1) (2018) © IOP Publishing LtdGoogle Scholar
  9. 9.
    Rajesh Kannan, V., Joseph, K.O.: Brain controlled mobile robot using brain wave sensor. IOSR J. VLSI Signal Process. (IOSR-JVSP) 77–82. e-ISSN: 2319–4200, p-ISSN : 2319–4197. www.iosrjournals.org; International Conference on Emerging Trends in Engineering and Technology Research, pp. 77–82
  10. 10.
    Ying, R., Weisz, J., Allen, P.K.: Grasping with your brain: a brain-computer interface for fast grasp selection. In: Bicchi, A., Burgard, W. (eds.) AG 2018 Robotics Research. In: Springer Proceedings in Advanced Robotics 2. Springer International Publishing, pp. 325–340.  https://doi.org/10.1007/978-3-319-51532-8_20Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  • Rheya Chakraborty
    • 1
  • Arup Kumar Chattopadhyay
    • 2
  • Animesh Kairi
    • 2
  • Mohuya Chakraborty
    • 2
  1. 1.Department of Electronics & Communication EngineeringInstitute of Engineering & ManagementSalt Lake, KolkataIndia
  2. 2.Department of Information TechnologyInstitute of Engineering & ManagementSalt Lake, KolkataIndia

Personalised recommendations