Electroencephalography-Based Recognition of Low Mental Resilience Using Multi-Condition Decision-Level Fusion Approach

Background: Mental resilience is an important indicator of our defence mechanism against mental illness. The assessment of mental resilience is conventionally done using psychological questionnaires but more recently, has been investigated using neuroimaging modalities such as the Magnetic Resonance Imaging and Positron Emission Tomography. While having high spatial resolution, these modalities might not be cost-effective and accessible to serve larger populations. This pilot trial investigates the performance of electroencephalography (EEG) based system to assess mental resilience under different mental conditions. Methods: A total of sixty-eight healthy adults took part in this trial. Three types of EEG features, namely spectra, functional connectivity (FC) and effective connectivity (EC) were extracted, and their correlation with a standard resilience assessment instrument – the Connor-Davidson Resilience Scale were evaluated at resting and task conditions using stepwise regression. The features with the best goodness of fit model were then used to classify individuals into a low and high mental resilience class. Results: The EC features using phase slope index achieved the highest adjusted R² and the lowest root mean square error, compared to the spectral and FC features. The SVM classifiers trained with the EC features were able to recognize low mental resilience with accuracy at least 66% depending on the mental condition. Fusion of SVM scores from the eyes-closed, eyes-open and task conditions improved the classification accuracy to more than 85%. Conclusion: The pilot trial reveals the EC as the most promising EEG feature type in assessing mental resilience due to its measure of causality in brain activity, and demonstrates that the fusion of decisions among different mental conditions can help improve the recognition of low mental resilience. Findings from this trial contribute to maturing an EEG-based resilience assessment system development for workplace settings.