The effects of varying levels of mental workload on motor imagery based brain-computer interface
by Bin Gu; Long Chen; Yufeng Ke; Yijie Zhou; Haiqing Yu; Kun Wang; Dong Ming
International Journal of Embedded Systems (IJES), Vol. 12, No. 3, 2020

Abstract: As one of the most applied EEG-based paradigms, motor imagery based brain-computer interface (MI-BCI) is used not only to control external devices, but also to help hemiplegic patients to reconstruct impaired motor function. However, in practical application of MI-BCI, users often face more varied external environments and complex cognitive activities, which could induce a high mental workload. This paper studied the effects of mental workload on motor imagery by designing a parallel task containing required motor and N-back task, taking motor execution as comparison. The experimental results showed that high mental workloads promoted the cognitive-motor process of motor imagery and restrained motor execution. Besides, the classification performance of MI-BCI was evaluated and compared at different mental workload levels between motor imagery and motor idle state. We also verified the possibility of detecting mental workload levels during motor imagery in offline analysis. The paper contributed to a wide range of MI-BCI applications and by exploring the cognitive-motor mechanism in motor imagery and execution.

Online publication date: Fri, 01-May-2020

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