Evaluation of cognitive workload from EEG during a mental arithmetic task

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. Copyright 2011 by Human Factors and Ergonomics Society, Inc. All rights reserved.