Abstract
The automated classification of a participant’s mental workload based on electroencephalographic (EEG) data is a challenging problem. Recently, network-based approaches have been introduced for this purpose. We seek to build on this work by introducing a novel feature extraction method for mental workload classification which uses multiplex networks formed from visibility graphs (VGs). The VG algorithm is an effective method for transforming a time series into a complex network representation. To analyze multivariate EEG time series, we construct multiplex temporal networks (MTNs), structures which contain the VGs of multiple EEG channels. We examine the tendency of layers in an MTN to share the same edges, referred to as layer entanglement. Using layer entanglement metrics as inputs, a support vector machine (SVM) classifier achieved an average 97% accuracy, 0.07 loss, and 99% F1 score in discrimination between two levels of cognitive workload based on EEG data. The findings presented here extend the findings from other recent studies that examined the prediction of mental workload. This work suggests that multiplex networks and layer entanglement can provide potential metrics for assessing mental workload with application to passive brain-computer interfaces.
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Acknowledgments
This work was supported by funding from the Maryland Space Grant Consortium and the University of Maryland College of Computer, Mathematical, and Natural Sciences Alumni Network.
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Teymourlouei, A., Stone, J., Gentili, R., Reggia, J. (2023). Multiplex Temporal Networks for Rapid Mental Workload Classification. In: Liu, F., Zhang, Y., Kuai, H., Stephen, E.P., Wang, H. (eds) Brain Informatics. BI 2023. Lecture Notes in Computer Science(), vol 13974. Springer, Cham. https://doi.org/10.1007/978-3-031-43075-6_18
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DOI: https://doi.org/10.1007/978-3-031-43075-6_18
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