Classification of Working Memory Load Using Wavelet Complexity Features of EEG Signals

We investigate the use of wavelet-based complexity measures of electroencephalogram (EEG) signals to evaluate changes in working memory load during the performance of a cognitive task with varying difficulty/load levels. Extracted wavelet-complexity measures associated with four entropic measures; that is Shannon, Tsallis, Escort-Tsallis and Renyi entropies demonstrate good discrimination among seven load levels imposed on the working memory with a classification rate of up to 96% using signals recorded from the frontal lobe of the brain. The extracted measures' values show a consistent decrease in the selected channels in two frontal and occipital lobes, as the memory load increases, indicating the EEGs disorder declines while the complexity grows. This illustrates that the brain behaves in a more organized manner characterized by more order and maximal complexity when dealing with higher load levels. The growing complexity can also reflect the higher activation of neural networks involved, as the task load increases.