Different Duty Cycle Ratio and Brightness Of Visual Stimuli Change To Steady State Visual Evoked Potential Response

Abstract

Stimuli types are very crucial for the performance of electroencephalogram (EEG) based brain computer interface (BCI) systems. This study aims to investigate methods for obtaining higher information transfer rate (ITR) through duty cycle and brightness variation of visual stimuli which have high frequency for steady state visual evoked potential-based BCI. Although previous studies were concentrated on either duty cycle or brightness of stimuli separately, our study focused on the change of duty cycle ratio and brightness of stimuli at the same time. Duty cycle values of 40%, 50%, and 60% were used. During the experiment, 16 flickering stimuli were used on liquid crystal display. Participants gazed to the flicker which had frequency of 15 Hz. Canonical correlation analyses (CCA) was used for channel selection and frequency detection. According to the CCA, the maximum average accuracy of the experiment was 92.54% when the frequency of flicker was in beta band and its duty cycle was 40% with a brightness tuning wave. Under the same conditions stated above, average ITR was improved 16.1% according to the most commonly used flicker model which is square wave and has 50% duty cycle.

Keywords

• Brain Computer Interface,EEG,Human-Computer Interaction and Systems,Steady State Visual Evoked Potential

References

1. [1] Weiskopf N., Mathiak K., Bock S.W., Scharnowski F., Veit R., Grodd W., Goebel R., and Birbaumer N., “Principles of a brain–computer interface (BCI) based on real-time functionalmagnetic resonance imaging (fMRI),” IEEE Trans. Biomed. Eng., vol. 51, no. 6, , Jun. 2004, pp. 966–970.
2. [2] Sitaram R., Caria A., and Birbaumer N., “Hemodynamic brain–computer interfaces for communication and rehabilitation,” Neural Netw., vol. 22, Nov. 2009, pp. 1320–1328.
3. [3] Muller-Putz G. R. and Pfurtscheller G., “Control of an electrical prosthesis with an SSVEP-based BCI,” IEEE Trans. Biomed. Eng., vol. 55, no. 1, Jan. 2008, pp. 361–364.
4. [4] Donchin E., Spencer K. V., and Wijesinghe R., “The mental prosthesis: Assessing the speed of a P300-based braincomputer interface,” IEEE Trans. Rehab. Eng., vol. 8(2), Jun. 2000, pp. 174-179.
5. [5] Birbaumer N., Kubler A., Ghanayim N., Hinterberger T., Perelmouter J., Kaiser J., Iversen I., Kotchoubey B., Neumann N., and Flor H., “The thought translation device (TTD) for completely paralyzed patients,” IEEE Trans. Rehab. Eng., vol. 8(2), , Jun. 2000, pp. 190-193.
6. [6] Pfurtscheller G., and Neuper C., “Motor imagery and direct brain computer communication,” Proc. IEEE, vol. 89(7), Jul. 2001.pp. 1123-1134.
7. [7] Zhang Y., Zhou G., Jin J., Wang X., and Cichocki A., “Optimizing spatial patterns with sparse filter bands for motor-imagery based brain-computer interface,” J. Neurosci. Methods, vol. 255, Nov. 2015, pp. 85-91.
8. [8] Wang Y., Wang R., Gao X., Hong B., and Gao S., "A practical VEP-based brain-computer interface," IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 14, 2006, pp. 234-240.

9. [9] Wang Y., Wang Y. T., and. Jung T. P, “Visual stimulus design for high-rate SSVEP BCI,” Electron Lett., vol. 46 (15) , Jul. 2010, pp. 1057-1058.
10. [10] Oralhan Z. and Tokmakçı M.. "The Effect of Duty Cycle and Brightness Variation of Visual Stimuli on SSVEP in Brain Computer Interface Systems." IETE Journal of Research (doi: 10.1080/03772063.2016.1176543), 1-9, 2016.
11. [11] Dal Seno B., Matteucci M., and Mainardi L.T., “The utility metric: A novel method to assess the overall performance of discrete braincomputer interfaces,” IEEE Trans. Neural Syst. Rehabil. Eng., vol. 18(1), Feb. 2010, pp. 2028.
12. [12] Wu Z., “The difference of SSVEP resulted by different pulse duty-cycle,” in Communications, Circuits and Systems ICCCAS 2009 International Conference, Milpitas, CA, Jul. 2009, pp. 605-607.
13. [13] Shyu K. K., Chiu Y. J., Lee P. L., Liang J.M., and Peng S. H., “Adaptive SSVEP-based BCI system with frequency and pulse duty-cycle stimuli tuning design,” IEEE Trans. Neural Syst. Rehabil. Eng., vol. 21(5), 2013 pp. 697-703.
14. [14] Manyakov N. V., Chumerin N., Robben A., Combaz A., Van Vliet M., and Van Hulle M. M., “Sampled sinusoidal stimulation profile and multichannel fuzzy logic classification for monitor-based phase-coded SSVEP braincomputer interfacing,” J. Neural Eng., vol. 10(3), Apr. 2013, pp. 115.
15. [15] Chen X., Chen Z., Gao S., and Gao X., “A high-ITR SSVEP-based BCI speller,” Taylor Francis Brain-Comput. Interfaces, vol. 1(34) , Sept. 2014, pp. 181-191.
16. [16] Lin Z., Zhang C., Wu W., and Gao X., “Frequency recognition based on canonical correlation analysis for SSVEPbased BCIs,” IEEE Trans. Biomed. Eng., vol. 53(12), Jun. 2007, pp.2610-2614.