Journal of the Institute of Convergence Signal Processing (융합신호처리학회논문지)

The Korea Institute of Convergence Signal Processing (한국융합신호처리학회)
권호 표지

The Optimization of Hybrid BCI Systems based on Blind Source Separation in Single Channel

단일 채널에서 블라인드 음원분리를 통한 하이브리드 BCI시스템 최적화

  • Yang, Da-Lin (Department of Electronics Engineering, Pukyong National University) ;
  • Nguyen, Trung-Hau (Department of Electronics Engineering, Pukyong National University) ;
  • Kim, Jong-Jin (Department of Electronics Engineering, Pukyong National University) ;
  • Chung, Wan-Young (Department of Electronics Engineering, Pukyong National University)
  • Received : 2018.01.19
  • Accepted : 2018.03.29
  • Published : 2018.03.31
Download PDF
⟨ Previous Next ⟩

Abstract

In the current study, we proposed an optimized brain-computer interface (BCI) which employed blind source separation (BBS) approach to remove noises. Thus motor imagery (MI) signal and steady state visual evoked potential (SSVEP) signal were easily to be detected due to enhancement in signal-to-noise ratio (SNR). Moreover, a combination between MI and SSVEP which is typically can increase the number of commands being generated in the current BCI. To reduce the computational time as well as to bring the BCI closer to real-world applications, the current system utilizes a single-channel EEG signal. In addition, a convolutional neural network (CNN) was used as the multi-class classification model. We evaluated the performance in term of accuracy between a non-BBS+BCI and BBS+BCI. Results show that the accuracy of the BBS+BCI is achieved $16.15{\pm}5.12%$ higher than that in the non-BBS+BCI by using BBS than non-used on. Overall, the proposed BCI system demonstrate a feasibility to be applied for multi-dimensional control applications with a comparable accuracy.

현재의 연구에서는 소음을 제거하기 위해 블라인드 소스 분리(BSS)접근 방식에 의해 최적화된 두뇌-컴퓨터 인터페이스(BCI)를 제안했다. 모터 이미지(MI)신호와 정상 상태 시각적 제거 전위(SSVEP)신호는 신호 대 잡음비(SNR)의 증가로 인해 쉽게 검출되었다. 또한, MI와 SSVEP사이의 조합은 일반적으로 현재 BCI에서 생성되는 명령 수를 증가시킬 수 있다. 현재 시스템은 계산 시간을 줄이고 BCI를 실제 용도에 가깝게 하기 위해 단일 채널 EEG신호를 사용했다. 또한, 복잡한 신경 네트워크(CNN)가 다중 클래스 분류 모델로 사용되었다. 우리는 비 MS/BCI와 BBS/BCI사이의 정확성 측면에서 성능을 평가했다. 결과적으로 BBS+BCI의 정확도는 비 BBS+BCI의 정확도보다 $16.15{\pm}25.12%$더 높은 수준에 도달했다. 사용하지 않을 때보다 BBS를 사용함으로써 전반적으로 제안된 BCI시스템은 비교적 정확한 다차원 제어 애플리케이션에 적용될 가능성을 입증했다.

Keywords

References

  1. T. Ebrahimi, J. M. Vesin, and G. Garcia, "Brain-computer interface in multimedia communication," IEEE Signal Processing Magazine, vol. 20, pp. 14-24, 2003.
  2. Nicolas-Alonso et al. "Brain computer interfaces, a review," Sensor, vol. 12(2), pp. 1211-1279, 2012. https://doi.org/10.3390/s120201211
  3. L.-W. Ko and S. S. K. Ranga, "Combining CCA and CFP for enhancing the performance in the hybrid BCI system," in 2015 IEEE Symposium Series on Computational Intelligence, pp. 103-108, Cape Town, South Africa, 2015.
  4. L.-W. Ko, S.-C. Lin, M.-S. Song, and O. Komarov, "Developing a few-channel hybrid BCI system by using motor imagery with SSVEP assist," in 2014 International JointConference on Neural Networks (IJCNN), pp. 4114-4120, Beijing, China, 2014.
  5. J. R. Wolpaw, N. Birbaumer, D. J. McFarland, G. Pfurtscheller, and T. M. Vaughan, "Brain-computer interfaces for communication and control," Clinical Neurophysiology, vol. 113, pp. 767-791, 2002. https://doi.org/10.1016/S1388-2457(02)00057-3
  6. FastICA MATLAB Package [Online]. Available: HTTP: http://www.cis.hut.fi/projects/ica/fastica
  7. A. Hyvarinen, "Survey on independent component analysis," Neural Computing Surveys, vol. 2, pp. 94-128, 1999
  8. C.W.N.F. Che Wan Fadzal, W. Mansor, L. Y. Khuan, "An Analysis of EEG Signal Generated From Grasping and Writing", In Proceedings of 2011 IEEE Conference on Computer Application & Industrial Electronics (ICCAIE 2011) , IEEE.
  9. Muller-Gerking J, Pfurtscheller G and Flyvbjerg H 1999 Designing optimal spatial filters for single-trial EEG classification in a movement task Clin. Neurophysiol. 110 787-98 https://doi.org/10.1016/S1388-2457(98)00038-8
  10. Hyvarinen, A., 1999. Fast and robust fixed-point algorithms for independent component analysis. IEEE T. Neural Netw., 10: 626-634. https://doi.org/10.1109/72.761722
  11. Sanjeev, N.J., et al., 2012. Blind source separation and ICA techniques: A review. Int. J. Eng. Sci. Technol., 4(04): 1490-1503
  12. Olyaee, S., M.S.E. Abadi, R. Ebrahimpour and M.R. Moradian, 2010. A comparative study of two blind source separation approaches to resolve the multisource limitation of the nutating rising-sun reticle based optical trackers. Int. J. Comput. Electr. Eng.,2(2): 1793-8163.
  13. Gao, J., Y. Yang, P. Lin, P. Wang and C. Zheng, 2010. Automatic removal of eye-movement and blink artifacts from EEG signals. Brain Topogr., 23:105-114. https://doi.org/10.1007/s10548-009-0131-4
  14. Nicolas-Alonso, L.F. and G.G. Jaime, 2012. Brain Computer Interfaces, a review. Sensors, 12: 1211-1279; DOI: 10.3390/s12020121
  15. L.-W. Ko, S.-C. Lin, M.-S. Song, and O. Komarov, "Developing a few-channel hybrid BCI system by using motor imagery with SSVEP assist," in 2014 International Joint Conference on Neural Networks (IJCNN), pp. 4114-4120, Beijing, China, 2014
  16. C. Brunner, B. Z. Allisona, D. J. Krusienskib, V. Kaisera, G. R. MullerPutza, G. Pfurtschellera, and C. Neupera, "Improved signal processing approaches in an online simulation of a hybrid brain-computer interface," J. Neurosci. Methods, vol. 188, no. 1, pp. 165-173, 2010. https://doi.org/10.1016/j.jneumeth.2010.02.002
  17. Yang D, Nguyen HT, Chung WY. An Online Synchronous Hybrid BCI System for Multidimensional Control Using MI and SSVEP. Summer Conference of IEIE, 2017 Jun:904-7.
  18. Jiang Y, Lee H, Li G, Chung WY. A Hybrid Brain-Computer Interface System for Multidimensional Control Using Motor Imagery and Eye Closure. Journal of Medical Imaging and Health Informatics. 2017 Nov 1;7(7):1580-8. https://doi.org/10.1166/jmihi.2017.2169