Fuzzy systems to process ECG and EEG signals for quantification of the mental workload

In this paper, we analyze the ECG and EEG signals by using a set of fuzzy systems developed for signal processing and try to see if one can quantify the mental workload based on the trend of EEG signal changes and the variation of pulse width from ECG signal. The signal data were taken from nuclear power plant operators while they perform the turbine operations. First, we apply a fuzzy system to extract the heart beat intervals from the ECG and a smoothing algorithm for the variation of the resulting heart beat intervals to obtain a trend variable. Next, we apply the same smoothing algorithm to the alpha-band frequencies and to the theta-band frequencies to get their trend curves. Finally, the three trend variables; the variation of the heart beat intervals, the alpha-band power, and the theta-band power are combined by a pair of fuzzy systems to estimate the mental workload during nuclear power plant operations. The results of applying our algorithm to three different data sets are included, along with a comparison between these results and the results obtained by applying a linear combination of the three variables. Compared results show that the 'mental workload' computed by fuzzy systems with nonlinear rules reflects the changes more clearly than the ones computed by linear functions. (C) 2002 Elsevier Science Inc. All rights reserved.