Browse > Article

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

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)
Publication Information
Journal of the Institute of Convergence Signal Processing / v.19, no.1, 2018 , pp. 7-13 More about this Journal
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.
Keywords
Brain-computer interface (BCI); Motor Imagery (MI); Steady State Visual Evoked Potential (SSVEP); Blind Source Separation (BBS); Convolutional Neural Network (CNN);
Citations & Related Records
연도 인용수 순위
  • Reference
1 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.   DOI
2 FastICA MATLAB Package [Online]. Available: HTTP: http://www.cis.hut.fi/projects/ica/fastica
3 A. Hyvarinen, "Survey on independent component analysis," Neural Computing Surveys, vol. 2, pp. 94-128, 1999
4 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.
5 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   DOI
6 Hyvarinen, A., 1999. Fast and robust fixed-point algorithms for independent component analysis. IEEE T. Neural Netw., 10: 626-634.   DOI
7 Sanjeev, N.J., et al., 2012. Blind source separation and ICA techniques: A review. Int. J. Eng. Sci. Technol., 4(04): 1490-1503
8 Nicolas-Alonso et al. "Brain computer interfaces, a review," Sensor, vol. 12(2), pp. 1211-1279, 2012.   DOI
9 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.
10 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.   DOI
11 Nicolas-Alonso, L.F. and G.G. Jaime, 2012. Brain Computer Interfaces, a review. Sensors, 12: 1211-1279; DOI: 10.3390/s12020121   DOI
12 T. Ebrahimi, J. M. Vesin, and G. Garcia, "Brain-computer interface in multimedia communication," IEEE Signal Processing Magazine, vol. 20, pp. 14-24, 2003.
13 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.
14 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.   DOI
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.   DOI
17 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.
18 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.