Abstract
Analyzing EEG signals can help us make implications about the user’s activities or even thoughts which can result in a myriad of applications. However, clinical EEG monitoring tools are expensive, often immobile and in need of professional supervision. Lately a couple of companies started the production of relatively cheap, easy-to-use, and mobile devices with significantly lower accuracy. In this paper, we intend to investigate the usability of these devices in recognizing selected basic activities e.g., winking, raising a hand etc., showing preliminary results on how clustering can prove to be an efficient method in labeling a low-quality EEG data set so that it could be used in supervised learning scenarios.
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Our data set will be made publicly available after publication so anyone can replicate the same algorithm or try new ones.
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Rudas, Á., Laki, S. (2020). Labeling Activities Acquired by a Low-Accuracy EEG Device. In: Barolli, L., Hellinckx, P., Natwichai, J. (eds) Advances on P2P, Parallel, Grid, Cloud and Internet Computing. 3PGCIC 2019. Lecture Notes in Networks and Systems, vol 96. Springer, Cham. https://doi.org/10.1007/978-3-030-33509-0_80
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DOI: https://doi.org/10.1007/978-3-030-33509-0_80
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