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Motor imagery and mental fatigue: inter-relationship and EEG based estimation

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Abstract

Even though it has long been felt that psychological state influences the performance of brain-computer interfaces (BCI), formal analysis to support this hypothesis has been scant. This study investigates the inter-relationship between motor imagery (MI) and mental fatigue using EEG: a. whether prolonged sequences of MI produce mental fatigue and b. whether mental fatigue affects MI EEG class separability. Eleven participants participated in the MI experiment, 5 of which quit in the middle because of experiencing high fatigue. The growth of fatigue was monitored using the Kernel Partial Least Square (KPLS) algorithm on the remaining 6 participants which shows that MI induces substantial mental fatigue. Statistical analysis of the effect of fatigue on motor imagery performance shows that high fatigue level significantly decreases MI EEG separability. Collectively, these results portray an MI-fatigue inter-connection, emphasizing the necessity of developing adaptive MI BCI by tracking mental fatigue.

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Notes

  1. https://en.wikipedia.org/wiki/Moving_average

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Acknowledgements

Financial support from MHRD as Centre of Excellence on Machine Learning Research and Big Data Analysis is gratefully acknowledged. Assistance received from DST-UKEIRI Project: DST/INT/UK/P-91/2014 is also acknowledged.

Author information

Authors and Affiliations

  1. Biomimetic and Cognitive Robotics Lab, Department of Computer Science and Engineering, Tezpur University, Tezpur, India

    Upasana Talukdar

  2. Mechatronics and Robotics Lab, Department of Mechanical Engineering, Indian Institute of Technology, Guwahati, India

    Shyamanta M. Hazarika

  3. School of Computer Science and Electronic Engineering, University of Essex, Essex, UK

    John Q. Gan

Authors
  1. Upasana Talukdar
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  2. Shyamanta M. Hazarika
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  3. John Q. Gan
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Correspondence to Upasana Talukdar.

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Action Editor: Sridevi Sarma

Appendix A

Appendix A

1.1 A.1 Visual Analogue Scale- Fatigue

Table 20 Visual Analogue Scale-Fatigue
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1.2 A.2 Chalder Fatigue Scale

Table 21 Chalder Fatigue Scale
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1.3 A.3 Fatigue Scale (FS) at the end of each run

Table 22 Fatigue Scale (FS) at the end of each run
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1.4 A.4 Plot of the trends of KPLS scores and subjective scores for runs 1–8

Fig. 16
figure 16

Fatigue scores estimated through EEG vs fatigue scores self reported by participants using “fatigue scale” for runs 1–8

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1.5 A.5 Plot of the trends of KPLS scores and subjective scores for runs 2–7

Fig. 17
figure 17

Fatigue scores estimated through EEG vs fatigue scores self reported by participants using “fatigue scale” on runs 2–7

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Talukdar, U., Hazarika, S.M. & Gan, J.Q. Motor imagery and mental fatigue: inter-relationship and EEG based estimation. J Comput Neurosci 46, 55–76 (2019). https://doi.org/10.1007/s10827-018-0701-0

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  • DOI: https://doi.org/10.1007/s10827-018-0701-0

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