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Visual stimuli for (a) experiment 1, (b) experiment 2a, and (c) experiment 2b. https://doi.org/10.1371/journal.pone.0226991.g001

Visual stimuli for (a) experiment 1, (b) experiment 2a, and (c) experiment 2b. https://doi.org/10.1371/journal.pone.0226991.g001

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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
... we propose an algorithm for a communication system that uses pupillometry and show its effectiveness when applied to an information-input interface in which multiple visual items are presented simultaneously (Experiment 2). As shown in Fig 1, experiment 2a proposes an interface with two response options (yes or no) and experiment 2b proposes an interface in the form of a numeric keypad with twelve response options. Experiment 1 presents a single circular visual stimulus in the center of a screen and modulates its luminance for each trial at one of the following frequencies: 0.25, 0.5, 0.75, 1.0, 1.25, 1.5, 1.75, 2.0, 2.25, 2.5, 2.75 or 3.0 Hz (Fig 1a). ...
Context 2
... shown in Fig 1, experiment 2a proposes an interface with two response options (yes or no) and experiment 2b proposes an interface in the form of a numeric keypad with twelve response options. Experiment 1 presents a single circular visual stimulus in the center of a screen and modulates its luminance for each trial at one of the following frequencies: 0.25, 0.5, 0.75, 1.0, 1.25, 1.5, 1.75, 2.0, 2.25, 2.5, 2.75 or 3.0 Hz (Fig 1a). The timedomain pupil-diameter data are analyzed by using a discrete Fourier transform (DFT) to quantify the characteristics of the pupil response to the luminance-modulation frequency. ...
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... 2 applies the algorithm developed in experiment 1. Experiment 2a allows us to evaluate the effectiveness of an input interface that offers two response options (yes or no) and experiment 2b does the same but for an interface with 12 response options (numerals or characters). Experiment 2a presents two items on a screen (Fig 1b) and the circle gazed at by the observer is identified by comparing the measured frequency of pupil-size oscillation with the luminancemodulation frequency of the various items in the interface. Experiment 2b presents twelve items (characters or numbers) on a screen (Fig 1c) and uses the same approach to identify the gaze-targeted item. ...
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... 2a presents two items on a screen (Fig 1b) and the circle gazed at by the observer is identified by comparing the measured frequency of pupil-size oscillation with the luminancemodulation frequency of the various items in the interface. Experiment 2b presents twelve items (characters or numbers) on a screen (Fig 1c) and uses the same approach to identify the gaze-targeted item. Herein we evaluate the accuracy of this input identification method. ...
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... Procedure. The circle was presented in the center of the screen (Fig 1a). The RGB values of this circle were modulated by a sine function to create a grayscale luminance modulation [see Eq (1)]. ...
Context 6
... interface is intended to be used for a communication device that accepts only two answers, such as yes or no. Two circles separated by a 1˚viewing angle appear side by side on the screen (Fig 1b) and serve as visual stimuli. The left circle is modulated one of the following eight luminance-modulation frequencies: f = 0.75, 1, 1.25, 1.5, 1.75, 2, 2.25, and 2.5 Hz. ...
Context 7
... interface is intended to be used as an input device (e.g., a numeral or character keyboard) with a small number of keys. Twelve circles were presented simultaneously on the screen (see Fig 1c), with a 1˚viewing-angle separation between the edges of adjacent circles. The luminance-modulation frequencies of the circles were, from left to right, 0.58, 0.70, 0.82 for the top row, 0.94, 1.06, 1.18 for the second row, 1.30, 1.42, 1.54 for the third row, and 1.66, 1.78, 1.90 Hz for the bottom row. ...
Context 8
... at the center of the gray screen appeared for 2 s a numeral or character, which instructed the participant to gaze at the corresponding circle during the following trial. The 12 stimuli (Fig 1c) were then presented for 8 s each, during which time the participants gazed at the predesignated circle. Prior to this experiment, each participant took part in a practice session. ...

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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.