Search

The accurate evaluation of operators' mental workload in human-machine systems plays an important role in ensuring the correct execution of tasks and the safety of operators. However, the performance of cross-task mental workload evaluation based on physiological metrics remains unsatisfactory. To explore the changes in dynamic functional connectivity properties with varying mental workload in different tasks, four mental workload tasks with different types of information were designed and a newly proposed dynamic brain network analysis method based on EEG microstate was applied in this paper. Six microstate topographies labeled as Microstate A-F were obtained to describe the task-state EEG dynamics, which was highly consistent with previous studies. Dynamic brain network analysis revealed that 15 nodes and 68 pairs of connectivity from the Frontal-Parietal region were sensitive to mental workload in all four tasks, indicating that these nodal metrics had potential to effectively evaluate mental workload in the cross-task scenario. The characteristic path length of Microstate D brain network in both Theta and Alpha bands decreased whereas the global efficiency increased significantly when the mental workload became higher, suggesting that the cognitive control network of brain tended to have higher function integration property under high mental workload state. Furthermore, by using a SVM classifier, an averaged classification accuracy of 95.8% for within-task and 80.3% for cross-task mental workload discrimination were achieved. Results implies that it is feasible to evaluate the cross-task mental workload using the dynamic functional connectivity metrics under specific microstate, which provided a new insight for understanding the neural mechanism of mental workload with different types of information.

To explore the effectiveness of using Electro- encephalogram (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.

Background: In steady state visual evoked potential (SSVEP)-based brain-computer interfaces, prolonged repeated flicker stimulation would reduce the system performance. To reduce the visual discomfort and fatigue, while ensuring recognition accuracy, and information transmission rate (ITR), a novel motion paradigm based on the steady-state motion visual evoked potentials (SSMVEPs) is proposed. Methods: The novel SSMVEP paradigm of the radial zoom motion was realized using the sinusoidal form to modulate the size of the stimuli. The radial zoom motion-based SSMVEP paradigm was compared with the flicker-based SSVEP paradigm and the SSMVEP paradigm based on Newton's ring motion. The canonical correlation analysis was used to identify the frequency of the eight targets, the recognition accuracy of different paradigms with different stimulation frequencies, and the ITR under different stimulation durations were calculated. The subjective comfort scores and fatigue scores, and decrease in the accuracy due to fatigue was evaluated. Results: The average recognition accuracy of the novel radial zoom motion-based SSMVEP paradigm was 93.4%, and its ITR reached 42.5 bit/min, which was greater than the average recognition accuracy of the SSMVEP paradigm based on Newton's ring motion. The comfort score of the novel paradigm was greater than both the flicker-based SSVEP paradigm and SSMVEP paradigm based on Newton's ring motion. The decrease in the recognition accuracy due to fatigue was less than that of the SSSMVEP paradigm based on Newton's ring motion. Conclusion: The SSMVEP paradigm based on radial zoom motion has high recognition accuracy and ITR with low visual discomfort and fatigue scores. The method has potential advantages in overcoming the performance decline caused by fatigue. [ABSTRACT FROM AUTHOR]