Abstract
EEG recording is a time consuming operation during which the subject is expected to stay still for a long time performing tasks. It is reasonable to expect some fluctuation in the level of focus toward the performed task during the task period. This study is focused on investigating various approaches for emphasizing regions of interest during the task period. Dividing the task period into three segments of beginning, middle and end, is expectable to improve the overall classification performance by changing the concentration of the training samples toward regions in which subject had better concentration toward the performed tasks. This issue is investigated through the use of techniques such as i) replication, ii) biasing, and iii) overlapping. A dataset with 4 motor imagery tasks (BCI Competition III dataset IIIa) is used. The results illustrate the existing variations within the potential of different segments of the task period and the feasibility of techniques that focus the training samples toward such regions.
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Atyabi, A., Fitzgibbon, S.P., Powers, D.M.W. (2012). Multiplication of EEG Samples through Replicating, Biasing, and Overlapping. In: Zanzotto, F.M., Tsumoto, S., Taatgen, N., Yao, Y. (eds) Brain Informatics. BI 2012. Lecture Notes in Computer Science(), vol 7670. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-35139-6_20
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DOI: https://doi.org/10.1007/978-3-642-35139-6_20
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