Evaluation of Mental Workload in Working Memory Tasks with Different Information Types Based on EEG

To explore the effectiveness of using Electroencephalogram (EEG) spectral power and multiscale sample entropy for accessing mental workload in different tasks, working memory tasks with different information types (verbal, object and spatial) and various mental loads were designed based on the N-Back paradigm. Subjective scores, accuracy and response time were used to verify the rationality of the tasks. EEGs from 18 normal adults were acquired when tasks were being performed, an independent component analysis (ICA) based artifact removal method were applied to get clean data. Linear (relative power in Theta and Alpha band, etc.) and nonlinear (multiscale sample entropy) features of EEGs were then extracted. Indices that can effectively reflect mental workload levels were selected by using multivariate analysis of variance statistical approach. Results showed that with the increment of task load, power of frontal Theta, Theta/Alpha ratio and sample entropies at scale more than 10 in parietal regions increased significantly first and decreased slightly then, while the power of central-parietal Alpha decreased significantly first and increased slightly then. Considering the difference between task types, no difference in power of frontal Theta, central-parietal Alpha and sample entropies at scales more than 10 of parietal regions were found between verbal and object tasks, as well as between two spatial tasks. No difference of frontal Theta/Alpha ratio was found in all the four tasks. The results can provide evidence for the mental workload evaluation in tasks with different information types.