Spectral EEG featuresfor evaluating cognitive load

This study was undertaken to investigate spectral features derived from EEG signals for measuring cognitive load. Measurements of this kind have important commercial and clinical applications for optimizing the performance of users working under high mental load conditions, or as cognitive tests. Based on EEG recordings for a reading task in which three different levels of cognitive load were induced, it is shown that a set of spectral features - the spectral entropy, weighted mean frequency and its bandwidth, and spectral edge frequency - are all able to discriminate the three load levels effectively. An interesting result is that spectral entropy, which reflects the distribution of spectral energy rather than its magnitude, provides very good discrimination between cognitive load levels. We also report those EEG channels for which statistical significance between load levels was achieved. The effect of frequency bands on the spectral features is also investigated here. The results indicate that the choice of optimal frequency band can be dependent on the spectral feature extracted.