Abstract: Objective Exploring the effectiveness of using EEG linear and nonlinear features for accessing mental workload in different tasks. Methods Working memory tasks with different information types and various mental loads were designed based on N-Back paradigm. EEG signals from 18 normal adults were acquired when tasks were being performed. Linear and nonlinear 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 With the increment of task load, power of frontal Theta, Theta/Alpha ratio, and sample entropies (scales>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. No difference in power of frontal Theta, central-parietal Alpha, and sample entropies (scales>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. Conclusion The results can provide evidence for the mental workload evaluation in tasks with different information types.