Abstract
We collected EEG data from 16 subjects while they performed a mental arithmetic task at five different levels of difficulty. A classifier was trained to discriminate between three conditions: relaxed, low workload and high workload, using spectral features of the EEG. We obtained an average classification accuracy of 62%. A continuous workload index was obtained by low-pass filtering the classifier’s output. The average correlation coefficient between the resulting workload index and the difficulty level of the task was 0.6.
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© 2011 Springer-Verlag Berlin Heidelberg
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Rebsamen, B., Kwok, K., Penney, T.B. (2011). EEG-Based Measure of Cognitive Workload during a Mental Arithmetic Task. In: Stephanidis, C. (eds) HCI International 2011 – Posters’ Extended Abstracts. HCI 2011. Communications in Computer and Information Science, vol 174. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-22095-1_62
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DOI: https://doi.org/10.1007/978-3-642-22095-1_62
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