# Investigation of Visual Stimulus Signals Using Hue Change for SSVEP
## 4 Discussion
These results indicate that the generation of visual stimuli by hue change can reduce the degree of fatigue caused by visual stimuli compared to the conventional method and can obtain the same level of discrimination rate. Next, we discuss the detailed discrimination rate for each frequency of each hue. The results for each hue are shown in Appendix A. Based on the results of red (0\({}^{\circ}\)) in Figure A1, the algorithm accuracy rate exceeded an average of 60% for all signals from R5 to R45, especially from 30 to 6 Hz, with small standard deviations and high mean values. An EEG analysis of, for example, a 15 Hz flash stimulus under gaze has been reported in [4] to reveal a large 15 Hz component, as well as a twofold and threefold harmonic, called a double frequency. Similarly, for 4 and 5 Hz, the algorithm accuracy rate may be reduced because of the misidentification of double frequencies of 10, 12, and 15 Hz. As the subjective questionnaire result of eye strain for the conventional method was 2.333 on average, we can say that the signals for R20-R45 have the same algorithm accuracy rate as the flash stimulus signals of the conventional method, with fatigue reduced by up to 15%. In addition, the subjective questionnaire result of eye fatigue for R5-R15 is approximately 1.5. Hence, the signal of R5-R15 is reduced eye fatigue by approximately 35.71% compared to the conventional method, and it is confirmed that the algorithm accuracy rate obtained is the same as that of the conventional method. Based on the results of green (120\({}^{\circ}\)) in Figure A3, the algorithm accuracy rate is low for G5 to G15 signals. This is because the flicker rating of the flash stimulation questionnaire, shown in Figure 9, shows that many subjects did not recognize the flash stimulation. Previous research in [14] reported that the SSVEP-induced EEG is not a physiological response to a stimulus but a change in the perception of that stimulus. Therefore, if the flash stimulus cannot be recognized, the SSVEP may not be obtained accurately. For signals G20-G45, the identification rate varied significantly by frequency: 70% on average at 30 Hz, 60% at 20 Hz, 40% at 15 Hz, 60% at 12 Hz, 70% at 10 Hz, 60% at 7.5 Hz, 65% at 6 Hz, 40% at 5 Hz, and 60% at 4 Hz. There are large differences in the algorithm accuracy rates,and it is not possible to obtain a stable algorithm accuracy rate. With respect to 5 Hz, the effect of double frequency may be considered, but with regard to the overall decrease in algorithm accuracy rate, the difficulty of recognizing color changes due to green flash stimuli is considered to have a major effect. Based on the blue (240\({}^{\circ}\)) results in Figure A5, the algorithm accuracy rate was 60% or more for 6-15 Hz but is approximately 50% for a signal above 20 Hz, which is a fast flash stimulus. In a previous report [15], blue and purple colors were reported to be indistinguishable. As a visual stimulus signal, the instantaneous flicker signal takes a long time to recognize the color change boundary; thus, a fast flash stimulus reduces the algorithm accuracy rate. Therefore, the identification rate was low at high-speed frequencies and high at low-speed frequencies. In addition, many answers that clearly recognized flickering were given in B10-B45 of flicker rating (Figure 11). Recognition of color changes is possible for any hue difference, and the difference in algorithm accuracy rates due to hue change is small. From these results, it is believed that the accuracy of blue varies depending on the frequency. From the results of the subjective questionnaire of eye strain in Figure 11, it is clear that many subjects experienced asthenopia in signals with strong color changes between B25 and B45. In summary, the blue signal cannot be identified with high accuracy in high-speed signals; therefore, it is considered that it can be used with low-speed signals as a stimulus signal to induce SSVEP.