Abstract
Reliable and accessible voting systems are essential for democratic societies as they are a vital link between the democratic representation and its citizens. The current voting systems need further accessibility features to aid people with disabilities to vote more independently. This paper describes “Neuro-Voting” a novel P300-based Brain-Computer Interface (BCI) voting application that allows the users to vote for their preferred candidate using their brain activity and without requiring any physical movement. Neuro-Voting uses the P300 wave activity elicited in the users to predict their vote. This paper discusses the design and implementation of the created system including the descriptions of the classification method that was implemented. The application is evaluated through a user study with five participants and the results show that it is highly accurate in predicting the votes of the participants. The application also received positive qualitative feedback from the participants after interacting with the system. The findings from this study demonstrates that it is possible for people to vote with their brains.
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Agarwal, R., Andujar, M. (2022). Neuro-Voting: An Accuracy Evaluation of a P300-Based Brain-Computer Interface for Casting Votes. In: Antona, M., Stephanidis, C. (eds) Universal Access in Human-Computer Interaction. Novel Design Approaches and Technologies. HCII 2022. Lecture Notes in Computer Science, vol 13308. Springer, Cham. https://doi.org/10.1007/978-3-031-05028-2_27
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