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Experiment 1: Average PSD of subjects as a function of luminance-modulation frequency of gaze-targeted visual stimulus.

Experiment 1: Average PSD of subjects as a function of luminance-modulation frequency of gaze-targeted visual stimulus.

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This study develops an information-input interface in which a visual stimulus targeted by a user’s eye gaze is identified based on the pupillary light reflex to periodic luminance modulations of the object. Experiment 1 examines how pupil size changes in response to periodic luminance modulation of visual stimuli, and the results are used to develo...

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Context 1
... the magnitude of the PSD peak depends on the luminance-modulation frequency of the visual stimulus. Therefore, to quantify the change in PSD frequency, we averaged the PSD peak over all the participants for each luminance-modulation frequency (Fig 4). The results show that the PSD peak decreases with increasing luminance-modulation frequency. ...
Context 2
... amplitude of pupil-size modulation is small in response to rapid luminance modulations[see, e.g., [6]]. Because the PSD analysis applied herein is based on the amplitude of pupil-size modulations [Eq(2)], the exponential decrease in Fig 4 is attributed to the differences in PSD amplitude at each frequency. Therefore, a meaningful comparison of PSD magnitudes in response to luminance-modulation frequencies would require a correction for the fundamental difference in the pupil response amplitude. ...
Context 3
... is the reciprocal of Eq (3), because the PSD varies exponentially with modulation frequency, as shown in Fig 4. We determine the parameters a and b in the exponential function f(x) [Eq (3)] from a nonlinear regression analysis of the data (see Fig 4). After this correction, the PSD results for the 12 frequencies are compared to estimate the frequency f 0 that produces the largest PSD (PSD max ). ...
Context 4
... is the reciprocal of Eq (3), because the PSD varies exponentially with modulation frequency, as shown in Fig 4. We determine the parameters a and b in the exponential function f(x) [Eq (3)] from a nonlinear regression analysis of the data (see Fig 4). After this correction, the PSD results for the 12 frequencies are compared to estimate the frequency f 0 that produces the largest PSD (PSD max ). ...

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... Hybrid BCI can improve the classification accuracy, increase the number of commands, and shorten the detection time of the BCI system by combining two or more patterns (at least one of which is a brain signal) (Hong and Jawad, 2017). Recently, pupillary responses (PR), such as the pupillary light reflex, have been used as the second pattern in addition to EEG due to the low user burden, non-invasiveness, and no need for training (Muto et al., 2020). Pupil diameter changes steadily with the illuminance of the observed object to regulate the amount of light entering the eye (Crawford, 1936;Woodhouse, 1975;Woodhouse and Campbell, 1975), and the modulation frequency of PR is synchronized with the luminance-modulation frequency of the visual stimulus. ...
... Pupil diameter changes steadily with the illuminance of the observed object to regulate the amount of light entering the eye (Crawford, 1936;Woodhouse, 1975;Woodhouse and Campbell, 1975), and the modulation frequency of PR is synchronized with the luminance-modulation frequency of the visual stimulus. The amplitude of PR decreases as the stimulation frequency increases (Muto et al., 2020), and the consistent, measurable PR can be induced at the flickering frequency up to 2.3 Hz (Naber et al., 2013). Compared with the detection of gaze position Yao et al., 2018), the measurement of PR does not require system calibration. ...
... They also established a binary communication based on PR and achieved an accuracy of 100% at 10 bpm and 96% at 15 bpm. Muto et al. (2020) realized an information input interface with 12 options (from 0.58 to 1.90 Hz, with an interval of 0.12 Hz) based on PR. The averaged power spectral density (PSD) peak decreased with increasing luminance-modulation frequency, and the averaged classification accuracy reached 85.4% with a data length of 7 s. ...
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Brain-computer interface (BCI) based on steady-state visual evoked potential (SSVEP) has been widely studied due to the high information transfer rate (ITR), little user training, and wide subject applicability. However, there are also disadvantages such as visual discomfort and “BCI illiteracy.” To address these problems, this study proposes to use low-frequency stimulations (12 classes, 0.8–2.12 Hz with an interval of 0.12 Hz), which can simultaneously elicit visual evoked potential (VEP) and pupillary response (PR) to construct a hybrid BCI (h-BCI) system. Classification accuracy was calculated using supervised and unsupervised methods, respectively, and the hybrid accuracy was obtained using a decision fusion method to combine the information of VEP and PR. Online experimental results from 10 subjects showed that the averaged accuracy was 94.90 ± 2.34% (data length 1.5 s) for the supervised method and 91.88 ± 3.68% (data length 4 s) for the unsupervised method, which correspond to the ITR of 64.35 ± 3.07 bits/min (bpm) and 33.19 ± 2.38 bpm, respectively. Notably, the hybrid method achieved higher accuracy and ITR than that of VEP and PR for most subjects, especially for the short data length. Together with the subjects’ feedback on user experience, these results indicate that the proposed h-BCI with the low-frequency stimulation paradigm is more comfortable and favorable than the traditional SSVEP-BCI paradigm using the alpha frequency range.