Abstract
A Brain-Computer Interface (BCI) is a technical system that creates a direct communication pathway between the human brain and an external device, such as a computer, without generally necessitating any physical movement. BCIs use various methods to detect and interpret brain activity, the most common being electroencephalography (EEG).
BCIs can be beneficial for people with disabilities since they provide an alternative communication and control method that can extend and replace traditional means, such as speech or muscular movements.
This paper investigates performance differences in code-modulated visual evoked potentials (cVEP) based BCI system between subjects of different gender identities. In this regard, the cVEP-based spelling interface with four targets was tested between two gender groups - 18 females and 18 males each, with ages ranging from 20 to 39 years. Three different spelling tasks were performed - writing of two command-balanced words and a pangram sentence, and cVEP stimuli were rendered on a monitor with a vertical refresh rate of 240 Hz.
Both groups (female and male) successfully completed all spelling tasks, achieving for the pangram task, a mean information transfer rate (ITR) of 29.38 bits per minute (bpm) and 28.09 bpm, respectively. Although the difference was not statistically significant for the pangram task, some recognizable differences were observed for the command-balanced tasks. Consequently, a trend (rather than a substantial difference) was realized between the male and female groups’ performance in the pangram task. Regarding the level of annoyance, subjects from both groups rated similar results on the visual stimulation setup.
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Acknowledgment
This research was supported by the German Federal Ministry of Education and Research funding program Forschung an Fachhochschulen under grant number 13FH033EX0.
The authors gratefully acknowledge the financial support by the association “The Friends of the University Rhine-Waal - Campus Cleve”.
We also appreciate each and every one of the research study’s participants as well as our student assistants.
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Volosyak, I., Adepoju, F., Stawicki, P., Rulffs, P., Cantürk, A., Henke, L. (2023). Gender Influence on cVEP-Based BCI Performance. In: Rojas, I., Joya, G., Catala, A. (eds) Advances in Computational Intelligence. IWANN 2023. Lecture Notes in Computer Science, vol 14135. Springer, Cham. https://doi.org/10.1007/978-3-031-43078-7_48
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DOI: https://doi.org/10.1007/978-3-031-43078-7_48
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