Characterizing mental load in an arithmetic task using entropy-based features

We propose the use of entropy-based features; spectral and approximate entropies, of recorded EEG signals to characterize mental load when performing a cognitive task. It is demonstrated on a seven load-level task that the spectral entropy is a good discriminator of mental load level and decreases consistently in accordance with the increased load. The extracted approximate entropy quantifies the irregularity of the EEGs, indicating a decrease in irregularity as the load increases. We also perform EEG source estimation to choose a smaller subset of EEG channels which make the most contribution in the load level discrimination. We conclude that the entropy-based features are capable of measuring the imposed mental load from the selected channels in two brain regions. This may demonstrate that the brain behaves in a more regular or focused manner when dealing with higher task loads. The efficacy of entropy-based features across frequency sub-bands is also investigated.