Cluster Computing

, Volume 22, Supplement 5, pp 11841–11848 | Cite as

FPGA based seizure detection and control for brain computer interface

  • S. TamilarasiEmail author
  • J. Sundararajan


The paper presents a brain computer interface system for patient monitoring to detect and correct seizure. About 4–7% of people are suffering from seizure and there are only less medical attention available. The objective of the work described in this paper is to detect and cure seizure automatically without physician intervention. The therapeutic device is a modeled chip using Field Programmable Gate Array Logic and SoC in which the size of the instrument is small. The EEG signals of the brain are recorded using scalp electrodes and converted to digital data. The EEG signal is preprocessed using quadrature spline wavelet transform. FPGA implementation of quadrature spline wavelet transform filter was done with Baugh Wooley and array multiplier. From the results it is found that the power and logic elements are improved when Baugh Wooley multiplier is used. But when delay is compared array multiplier has better performance. The preprocessed data is feature extracted using double stage pattern search method where the seizure event is detected. Once the seizure is detected, the seizure control block is activated. It controls the seizure in few seconds. For seizure detection and correction, adaptive methods and hardware simulators are used. The performance and efficiency is compared for various devices using Quartus in FPGA cyclone II and III.


EEG Seizure FPGA Brain computer interface Wavelet transform Array multiplier Baugh Wooley methods 


  1. 1.
    Kais, B., Ghaffari, F., Romain, O., Djemal, R.: An embedded implementation of home devices control system based on brain computer interface. In: Proceeding 26th International Conference on Microelectronics (ICM); pp. 140–143 (2014)Google Scholar
  2. 2.
    Khurana, K., Gupta, P., Panicker, R.C., Kumar, A.: Development of an FPGA-based real-time P300 speller. In: Proceedings of the 22nd International Conference on Field Programmable Logic and Applications (FPL); pp. 551–554 (2012)Google Scholar
  3. 3.
    Shyu, K.-K., Lee, M.-H., Lee, P.-L., Chiu, Y.-J.: The low-cost implement of a phase coding SSVEP-Based BCI system. In: Proceedings of the 17th IEEE International Conference on Electronics, Circuits and Systems; pp 559–562 (2010)Google Scholar
  4. 4.
    Feng, C.-W., Hu, T.-K., Chang, J.-C., Fang, W.-C.: A reliable brain computer interface implemented on an FPGA for a mobile dialing system. In: Proceedings of the IEEE International Symposium on Circuits and Systems (ISCAS); pp. 654–657 (2014)Google Scholar
  5. 5.
    Shyu, K.-K., Chiu, Y.-J., Lee, P.-L., Lee, M.-H., Sie, J.-J., Wu, C.-H., Wu, Y.-T., Tung, P.-C.: Total design of an FPGA-based brain-computer interface control hospital bed nursing system. Proc. IEEE Trans. Ind. Electron. 60(7), 2731–2739 (2013)CrossRefGoogle Scholar
  6. 6.
    Elsayed, N., Zaghloul, Z.S., Bayoumi, M.: Brain computer interface: EEG signal preprocessing issues and solutions. Int. J. Comput. Appl. 169(3), 12–16 (2017)Google Scholar
  7. 7.
    Wang, T.-H., Huang, P.-T., Chen, K.-N., Chiou, J.-C., Chen, K.-H., Chiu, C.-T., Tong, H.-M., Chuang, C.-T., Hwang, W.: Energy-efficient configurable discrete wavelet transform for neural sensing applications. In: Proceedings of the IEEE International Symposium on Circuits and Systems (ISCAS); pp. 1841–1844 (2014)Google Scholar
  8. 8.
    Gagnon-Turcotte, G., Gosselin, B.: Multichannel spike detector with an adaptive threshold based on a Sigma-delta control loop. In: Proceedings of the 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC); pp. 7123–7126 (2015)Google Scholar
  9. 9.
    Hsieh, C.-H., Chu, H.-P., Huang, Y.-H. : An HMM-based eye movement detection system using EEG brain-computer interface. In: Proceedings of the IEEE International Symposium on Circuits and Systems (ISCAS); pp. 662–665 (2014)Google Scholar
  10. 10.
    Lee, S.-C., Chen, T.-J., Chiueh, H.: A multi-channel multi-mode physiological signals acquisition and analysis platform. In: Proceedings of the IEEE International Symposium on Circuits and Systems (ISCAS2013); pp. 397–400 (2013)Google Scholar
  11. 11.
    Agrawal, M., Vidyashankar, S., Huang, K.: On-chip implementation of ECoG signal data decoding in brain-computer interface. In: Proceedings of the IEEE 21st International Mixed-Signal Testing Workshop (IMSTW); pp. 1–6 (2016)Google Scholar
  12. 12.
    Ambroise, M., Levi, T., Bornat, Y., Saighi, S.: Biorealistic spiking neural network on FPGA. In: Proceedings of the 47th Annual Conference on Information Sciences and Systems (CISS); pp. 1–6 (2013)Google Scholar
  13. 13.
    Wijesinghe, L.P., Wickramasuriya, D.S., Pasqual, Ajith A.: A generalized preprocessing and feature extraction platform for scalp EEG signals on FPGA. In: Proceedings of the IEEE Conference on Biomedical Engineering and Sciences (IECBES); pp. 137–142 (2014)Google Scholar
  14. 14.
    Dilranjan, S., Wickramasuriya, L.P., Wijesinghe, S.M.: Seizure prediction using Hilbert Huang transform on field programmable gate array. In: Proceedings of the IEEE Global Conference on Signal and Information Processing (GlobalSIP); pp. 933–937 (2015)Google Scholar
  15. 15.
    Won, M., Albalawi, H., Li, X., Thomas, D.E.: Low-power hardware implementation of movement decoding for brain computer interface with reduced-resolution discrete cosine transform. In: Proceedings of the 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society; pp. 1626–1629 (2014)Google Scholar
  16. 16.
    Zhang, F., Aghagolzadeh, M., Oweiss, K.: A low-power implantable neuroprocessor on nano-FPGA for brain machine interface applications. In: Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP); pp. 1593–1596 (2011)Google Scholar
  17. 17.
    Aravind, M., Suresh, B.S.: Embedded implementation of brain computer interface concept using FPGA. In: Proceedings of the International Conference on Emerging Technological Trends (ICETT); pp. 1–5 (2016)Google Scholar
  18. 18.
    Mountney, J., Obeid, I., Silage, D.: Modular particle filtering FPGA hardware architecture for brain machine interfaces. In: Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society; pp. 4617–4620 (2011)Google Scholar
  19. 19.
    Tariqus Salam, M., Velazquez, J.L.P., Genov, R.: Seizure suppression efficacy of closed-loop versus open-loop deep brain stimulation in a rodent model of epilepsy. IEEE Trans. Neural Syst. Rehabilit. Eng. 24(6), 710–719 (2016)Google Scholar
  20. 20.
    Shahdoost, S., Mohseni, P.: An FPGA platform for generation of stimulus triggering based on intracortical spike activity in brain-machine-body interface (BMBI) applications. In: Proceedings of the IEEE International Symposium on Circuits and Systems (ISCAS); pp. 1766–1769 (2015)Google Scholar
  21. 21.
    Shyu, K.-K., Lee, P.-L., Lee, M.-H., Lee, M.-H., Yun-Jen, C.: Development of a low-cost FPGA-based SSVEP BCI multimedia control system. Proc. IEEE Trans. Biomed. Circuit Syst. 4(2), 125–132 (2010)CrossRefGoogle Scholar
  22. 22.
    Tamilarasi, S., Sundararajan, J.: A novel implementation of seizure control stimulator for brain computer interface. Int. J. Print. Packag. Allied Sci. 5(1), 897–905 (2017)Google Scholar
  23. 23.
    Kim, S., Tathireddy, P., Normannc, R.A., Solzbacher, F.: Thermal impact of an active 3-D microelectrode array implanted in the brain. Proc. IEEE Trans. Neural Syst. Rehabil. Eng 15(4), 493–501 (2007)CrossRefGoogle Scholar
  24. 24.
    Yoo, J., Yan, L., Damak, D.E., Altaf, M.A.B., Shoeb, A.H., Chandrakasan, A.P.: An 8-channel scalable EEG acquisition SoC with patient-specific seizure classification and recording processor. Proc. IEEE J. Solid-State Circuit 48(1), 214–228 (2013)CrossRefGoogle Scholar
  25. 25.
    Salam, M., Sawan, M., Nguyen, D.: A novel low-power-implantable epileptic seizure-onset detector. Proc. IEEE Trans. Biomed. Circuits Syst. 5(6), 568–578 (2011)CrossRefGoogle Scholar
  26. 26.
    Azin, M., Guggenmos, D., Barbay, S., Nudo, R., Mohseni, P.: A battery powered activity dependent intracortical microstimulation IC for brain machine brain interface. Proc. IEEE J. Solid-State Circuits 46(4), 731–745 (2011)CrossRefGoogle Scholar
  27. 27.
    Liang, Sheng-Fu, Wang, Hsu-Chuan, Chang, Wan-Lin: Combination of EEG complexity and spectral analysis for epilepsy diagnosis and seizure detection. EURASIP Journal on Advances in Signal Processing; p. 1-15 (2010)Google Scholar

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Authors and Affiliations

  1. 1.Cheran College of EngineeringKarurIndia
  2. 2.Pavai College of TechnologyNamakkalIndia

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