Abstract
We collected electroencephalographic (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 correlation coefficient between the resulting workload index and the difficulty level of the task was 0.6 on average.
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