Effects of Luminosity Contrast and Stimulus Duration on User Performance and Preference in a P300-Based Brain–Computer Interface

Abstract

Brain-computer interfaces (BCI) have potential to provide a new channel of communication and control for people with severe motor disabilities. Although many empirical studies exist, few have specifically evaluated the impact of contributing factors on user performance and perception in BCI applications, especially for users with motor disabilities. This article reports the effects of luminosity contrast and stimulus duration on user performance and usage preference in a P300-based BCI application, P300 Speller. Ten participants with neuromuscular disabilities (amyotrophic lateral sclerosis and cerebral palsy) and 10 able-bodied participants were asked to spell six 10-character phrases in the P300 Speller. The overall accuracy was 76.5% for the able-bodied participants and 26.8% for participants with motor disabilities. The results showed that luminosity contrast and stimulus duration have significant effects on user performance. In addition, participants preferred high luminosity contrast with middle or short stimulus duration. However, these effects on user performance and preference varied for participants with and without motor disabilities. The results also indicated that although most participants with motor disabilities can establish BCI control, BCI illiteracy does exist. These results of the study should provide insights into the future research of the BCI systems, especially the real-world applicability of the BCI applications as a nonmuscular communication and control system for people with severe motor disabilities.

Rapid P300 brain-computer interface communication with a head-mounted display

“…Whether larger stimuli in a visual “single stimulus” paradigm elicit larger P300 amplitudes has not yet been systematically investigated. Li et al ( 2011 , 2014 ) investigated the effect of screen size and stimulus luminosity with a P300 BCI and found that a computer monitor elicited a larger P300 as compared to a cell phone display and reported a higher P300 with increased luminosity of the stimuli, the latter effect, however, was not significant. Similar P300 amplitudes were elicited by a visual oddball and a visual “single stimulus” paradigm (Mertens and Polich, 1997 ).…”

Investigation of Visual Stimulus With Various Colors and the Layout for the Oddball Paradigm in Evoked Related Potential-Based Brain–Computer Interface

“…The present study mainly focused on the effect of chromatic stimulus on the performance of an ERP-based BCI and discussed several related problems stated as follows: (1) the influence on the offline error trials brought by the layout and the relationship between the layout and adjacency distraction, (2) the availability of the conclusion (Li et al, 2014) applied to the present study that better performance (including higher accuracy, higher amplitude, and shorter latency) occurred in a high-luminosity contrast, and (3) the observation of the ERP components' waveforms in this work.…”

“…Li et al (2014) investigated the effects of luminosity contrast on BCI performance. It was reported that higher classification accuracy was achieved by a high-luminosity contrast; higher amplitude and shorter latency of VEP P300 were also released by the high-luminosity contrast stimulus.…”

“…Green (onset)/blue (offset) stimulus yielded a better practical performance in P300-BCI than white/gray stimulus (Takano et al, 2009). Moreover, the luminosity contrast was also investigated for P300 speller (Li et al, 2014). The RGB colors acting as stimuli have been utilized to compare EEG classification algorithms or feature extraction methods (Rasheed and Marini, 2015; Alharbi et al, 2016).…”

Riemannian geometry for EEG-based brain-computer interfaces; a primer and a review

“…III) The improvement of the BCI interface by studying its properties [19][20][21][22]. For P300-based BCI these lines of research have led to, inter varia, the introduction of language models for letter and word prediction [23][24][25], automatic pause detection [26], the use of faces for flashing symbols [24], the use of random groups or pseudo-random groups flashing instead of row-column flashing [27][28][29][30], the use of inter-stimulus intervals randomly drawn from an exponential distribution instead of constant [27], the dynamic stopping of flashing sequences [25,31], etc.…”