نشریه علمی مهندسی پزشکی زیستی

Fatigue Assessment using Frequency Features in SSVEP-based Brain-Computer Interfaces

Document Type : Full Research Paper

Authors

1 Ph.D. Student, Biomedical Engineering Department, Semnan University, Semnan, Iran

2 Associate Professor, Biomedical Engineering Department, Semnan University, Semnan, Iran

Abstract
A significant challenge in moving SSVEP-based BCIs from the laboratory into real-life applications is that the user may suffer from fatigue. Prolonged execution of commands in a BCI system can cause mental fatigue and, as a result, create dissatisfaction in the user and reduce the system's efficiency. The first step to studying and ultimately reducing the destructive effects of fatigue is to identify the level of fatigue. Although frequency indices have been used for fatigue evaluation, the results of previous research in this field are inconsistent. Therefore, there is no detailed and comprehensive investigation of how fatigue affects frequency indices. In this paper, the evaluation of frequency-domain fatigue indicators has been done accurately and comprehensively. For this purpose, nine visual stimuli with different flickering frequencies were displayed to the subject, and they were asked to pay attention to the target cue. The visual stimulation presented continuously, without rest to ensure that the fatigue occurs at the end of the test. Mean amplitude of theta, alpha, and beta bands, and 4-30 Hz frequency band segments with 1 Hz, 2 Hz, and 4 Hz steps were evaluated as fatigue indices. The results show that the mean amplitude of the frequency band of 8-9 Hz is more suitable for fatigue evaluation. This index has the most changes with fatigue in a state of wakeful relaxation of the subject and the mental effort to maintain the level of alertness in the fatigue state.

Keywords

Subjects


  1. Shu et al., “Tactile Stimulation Improves Sensorimotor Rhythm-Based BCI Performance in Stroke Patients,” IEEE Trans. Biomed. Eng., vol. 66, no. 7, pp. 1987–1995, 2019, doi: 10.1109/TBME.2018.2882075.
  2. Sadeghi and A. Maleki, “Accurate estimation of information transfer rate based on symbol occurrence probability in brain-computer interfaces,” Biomed. Signal Process. Control, vol. 54, p. 101607, 2019, doi: 10.1016/j.bspc.2019.101607.
  3. Peng, C. M. Wong, Z. Wang, A. C. Rosa, H. T. Wang, and F. Wan, “Fatigue Detection in SSVEP-BCIs Based on Wavelet Entropy of EEG,” IEEE Access, vol. 9, pp. 114905–114913, 2021, doi: 10.1109/ACCESS.2021.3100478.
  4. G. Pinheiro, E. L. M. Naves, P. Pino, E. Losson, A. O. Andrade, and G. Bourhis, “Alternative communication systems for people with severe motor disabilities: a survey,” Biomed. Eng. Online, vol. 10, no. 1, pp. 1–28, 2011.
  5. Zheng et al., “Anti-fatigue Performance in SSVEP-Based Visual Acuity Assessment: A Comparison of Six Stimulus Paradigms,” Front. Hum. Neurosci., vol. 14, no. July, pp. 1–11, 2020, doi: 10.3389/fnhum.2020.00301.
  6. Xie, G. Xu, J. Wang, M. Li, C. Han, and Y. Jia, “Effects of mental load and fatigue on steady-state evoked potential based brain computer interface tasks: A comparison of periodic flickering and motion-reversal based visual attention,” PLoS One, vol. 11, no. 9, pp. 1–15, 2016, doi: 10.1371/journal.pone.0163426.
  7. Peng et al., “Fatigue Evaluation Using Multi-Scale Entropy of EEG in SSVEP-Based BCI,” IEEE Access, vol. 7, pp. 108200–108210, 2019, doi: 10.1109/ACCESS.2019.2932503.
  8. J. Cho, E. Costa, P. R. Menezes, T. Chalder, D. Bhugra, and S. Wessely, “Cross-cultural validation of the Chalder Fatigue Questionnaire in Brazilian primary care,” J. Psychosom. Res., vol. 62, no. 3, pp. 301–304, 2007, doi: 10.1016/j.jpsychores.2006.10.018.
  9. Cao, F. Wan, C. M. Wong, J. N. da Cruz, and Y. Hu, “Objective evaluation of fatigue by EEG spectral analysis in steady-state visual evoked potential-based brain-computer interfaces,” Biomed. Eng. Online, vol. 13, no. 1, pp. 1–13, 2014, doi: 10.1186/1475-925X-13-28.
  10. Lal, A. Craig, P. Boord, L. Kirkup, H. N.-J. of safety Research, and undefined 2003, “Development of an algorithm for an EEG-based driver fatigue countermeasure,” Elsevier, Accessed: Aug. 14, 2022. [Online]. Available: https://www.sciencedirect.com/science/article/pii/S0022437503000276
  11. Chen, K. Li, Q. Wu, H. Wang, Z. Qian, and G. Sudlow, “EEG-based detection and evaluation of fatigue caused by watching 3DTV,” Displays, vol. 34, no. 2, pp. 81–88, 2013, doi: 10.1016/j.displa.2013.01.002.
  12. W. Hsu and M. J. J. Wang, “Evaluating the effectiveness of using electroencephalogram power indices to measure visual fatigue,” Percept. Mot. Skills, vol. 116, no. 1, pp. 235–252, 2013, doi: 10.2466/29.15.24.PMS.116.1.235-252.
  13. Zou, Y. Liu, M. Guo, and Y. Wang, “EEG-Based Assessment of Stereoscopic 3D Visual Fatigue Caused by Vergence-Accommodation Conflict,” J. Disp. Technol., vol. 11, no. 12, pp. 1076–1083, 2015, doi: 10.1109/JDT.2015.2451087.
  14. -Y. Cheng and H.-T. Hsu, “Mental Fatigue Measurement Using EEG,” Risk Manag. Trends, 2011, doi: 10.5772/16376.
  15. Ruan, Kun Xue, and Mingai Li, “Feature extraction of SSVEP-based brain-computer interface with ICA and HHT method,” in Proceeding of the 11th World Congress on Intelligent Control and Automation, Jun. 2014, vol. 2015-March, no. March, pp. 2418–2423. doi: 10.1109/WCICA.2014.7053100.
  16. Chalder et al., “Development of a fatigue scale,” Elsevier, vol. 37, no. 2, pp. 147–153, 1993, Accessed: Aug. 14, 2022. [Online]. Available: https://www.sciencedirect.com/science/article/abs/pii/002239999390081P
  17. M. G. Tello, S. M. T. Muller, T. Bastos-Filho, and A. Ferreira, “A comparison of techniques and technologies for SSVEP classification,” ISSNIP Biosignals Biorobotics Conf. BRC, 2014, doi: 10.1109/BRC.2014.6880956.
  18. Sadeghi and A. Maleki, “Adaptive canonical correlation analysis for harmonic stimulation frequencies recognition in SSVEP-based BCIs,” Turkish J. Electr. Eng. Comput. Sci., vol. 27, no. 5, pp. 3729–3740, 2019, doi: 10.3906/elk-1805-32.
  19. Ziafati and A. Maleki, “Boosting the Evoked Response of Brain to Enhance the Reference Signals of CCA Method,” IEEE Trans. Neural Syst. Rehabil. Eng., vol. 30, pp. 2107–2115, 2022, doi: 10.1109/TNSRE.2022.3192413.
  20. Misirlisoy E. Programming Behavioral Experiments with MATLAB and Psychtoolbox: 9 Simple Steps for Students and Researchers. Routledge; 2016 Nov 10.
  21. صادقی, مالکی. روش بهبود یافته تحلیل همبستگی متعارف برای بازشناسی فرکانس پتانسیل برانگیخته بینایی حالت ماندگار. مدل سازی در مهندسی، 2018، 16 (55)، 199-207.
  22. Wu Z, Lai Y, Xia Y, Wu D, Yao D. Stimulator selection in SSVEP-based BCI. Medical engineering & physics. 2008 Oct 1;30(8):1079-88.
  23. Foong R, Ang KK, Quek C, Guan C, Phua KS, Kuah CW, Deshmukh VA, Yam LH, Rajeswaran DK, Tang N, Chew E. Assessment of the efficacy of EEG-based MI-BCI with visual feedback and EEG correlates of mental fatigue for upper-limb stroke rehabilitation. IEEE Transactions on Biomedical Engineering. 2019 Jun 5;67(3):786-95.
  24. Eoh HJ, Chung MK, Kim SH. Electroencephalographic study of drowsiness in simulated driving with sleep deprivation. International Journal of Industrial Ergonomics. 2005 Apr 1;35(4):307-20.
  25. Sadeghi S, Maleki A. A comprehensive benchmark dataset for SSVEP-based hybrid BCI. Expert Systems with Applications. 2022 Aug 15;200:117180.
  26. Delorme A, Makeig S. EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis. Journal of neuroscience methods. 2004 Mar 15;134(1):9-21.
Volume 16, Issue 3
Autumn 2022
Pages 219-229

  • Receive Date 22 August 2022
  • Revise Date 07 February 2023
  • Accept Date 20 March 2023