The Interrelation Between Fatigue and Other Mental States in BCI-HMD Motor Imagery Task

Abstract

Introduction

Brain-compute interface (BCI) utilizes electroencephalographic (EEG) brain signals to create direct control and communication between the brain and a machine or computer to research neurophysiological phenomena. The fluctuation of mental states may decrease the performance of BCI models, making them less usable. Mental states such as mental fatigue, cognitive load, and sleepiness can be observed through EEG by assessing wavebands at specific locations [1]. The increase of occipital and parietal alpha EEG rhythm and the increase of the frontal delta rhythm have been associated with mental fatigue and sleepiness. While approximately 17 minutes of motor imagery (MI) produces positive effects on motor abilities in neurorehabilitation, mental fatigue develops already after 30 minutes [2]. A study by Talukdar and colleagues found a significant increase of spectral EEG power in the range of 0.1-12 Hz after performing the motor imagery (MI) task, suggesting increased mental fatigue [3].

Methods

The current study attempts to evaluate the progression of mental fatigue and other mental states (cognitive load, frustration, motivation) after performing MI task for the upper limbs in BCI combined with a head-mounted display (BCI-HMD) system by acquiring a range of electrophysiological, behavioral, and subjective measures in young healthy participants. These measures include questionnaires examining mental fatigue, cognitive load, motivation, and cybersickness, a Continuous Performance Test (CPT) to examine sustained attention, and neural correlates acquired through EEG during the resting states. By collecting a selection of measures, we aim to evaluate the influence of low cognitive workload task on mental fatigue and its interaction with other mental states.

Hypothesis

It is hypothesized that after performing the MI task, the participants will report feeling mentally fatigued, their reaction time in CPT will increase, and frontal EEG delta as well as the occipital and parietal EEG alpha band will increase. Additionally, the participants may report feeling less motivated, however, their cognitive MI performance would not be influenced.

References

[1] L. J. Trejo, K. Kubitz, R. Rosipal, R. L. Kochavi, and L. D. Montgomery, “EEG-Based Estimation and Classification of Mental Fatigue,” PSYCH, vol. 06, no. 05, pp. 572–589, 2015, doi: 10.4236/psych.2015.65055.

[2] V. Rozand, F. Lebon, P. J. Stapley, C. Papaxanthis, and R. Lepers, “A prolonged motor imagery session alter imagined and actual movement durations: Potential implications for neurorehabilitation,” Behavioural Brain Research, vol. 297, pp. 67–75, Jan. 2016, doi: 10.1016/j.bbr.2015.09.036.

[3] U. Talukdar, S. M. Hazarika, and J. Q. Gan, “Motor imagery and mental fatigue: inter-relationship and EEG based estimation,” J Comput Neurosci, vol. 46, no. 1, pp. 55–76, Feb. 2019, doi: 10.1007/s10827-018-0701-0.