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    Measuring Mental Workload Using ERPs Based on FIR, ICA, and MARA

    Yu Sun1, Yi Ding2,*, Junyi Jiang3, Vincent G. Duffy4

    Computer Systems Science and Engineering, Vol.41, No.2, pp. 781-794, 2022, DOI:10.32604/csse.2022.016387

    Abstract Mental workload is considered to be strongly linked to human performance, and the ability to measure it accurately is key for balancing human health and work. In this study, brain signals were elicited by mental arithmetic tasks of varying difficulty to stimulate different levels of mental workload. In addition, a finite impulse response (FIR) filter, independent component analysis (ICA), and multiple artifact rejection algorithms (MARAs) were used to filter event-related potentials (ERPs). Then, the data consisting of ERPs, subjective ratings of mental workload, and task performance, were analyzed through the use of variance and Spearman’s… More >

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