Towards a versatile mental workload modeling using neurometric indices
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Yamini Gogna
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Sheela Tiwari
Abstract
Researchers have been working to magnify mental workload (MWL) modeling for a long time. An important aspect of its modeling is feature selection as it interprets bulky and high-dimensional EEG data and enhances the accuracy of the classification model. In this study, a feature selection technique is proposed to obtain an optimized feature set with multiple domain features that can contribute to classifying the MWL at three distinct levels. The brain signals from thirteen healthy subjects were examined while they attended an intrinsic MWL of spotting differences in a set of similar pictures. The Recursive Feature Elimination (RFE) technique selects the robust features from the feature matrix by eliminating all the least contributing features. Along with the Support Vector Machine (SVM), the overall classification accuracy with the proposed RFE reached 0.913 from 0.791 surpassing the other techniques mentioned. The results of the study also significantly display the variation in the mean values of the selected features at the three workload levels (p<0.05). This model can become the principle for defining the workload level quantification applicable to diverse fields like neuroergonomics study, intelligent assistive devices (ADs) development, blue-chip technology exploration, cognitive evaluation of students, power plant operators, traffic operators, etc.
Acknowledgments
We are deeply grateful to the participants for patiently upholding the experiment followed by the feedback.
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Research funding: None declared.
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Author contributions: All authors have accepted responsibility for the entire content of this manuscript and approved its submission. Conceptualization, Methodology, Software: Yamini Gogna, Dr. Sheela Tiwari, Dr. Rajesh Singla; Data curation, Writing- Original draft preparation: Yamini Gogna; Visualization, Investigation: Yamini Gogna, Dr. Rajesh Singla; Supervision: Dr. Sheela Tiwari; Software, Validation: Yamini Gogna; Writing- Reviewing and Editing: Dr. Sheela Tiwari, Dr. Rajesh Singla.
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Competing interests: Authors state no conflict of interest.
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Informed consent: Informed consent was obtained from all individuals included in this study.
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Ethical approval: The local Institutional Review Board deemed the study exempt from review.
References
1. Longo, L, Orrú, G. Evaluating instructional designs with mental workload assessments in university classrooms. Behav Inf Technol 2020;41:1–31. https://doi.org/10.1080/0144929X.2020.1864019.Suche in Google Scholar
2. Seitz, M, Daun, TJ, Zimmermann, A, Lienkamp, M. Measurement of electrodermal activity to evaluate the impact of environmental complexity on driver workload. In: Proceedings of the FISITA 2012 world automotive congress: Springer; 2013:245–56 pp.10.1007/978-3-642-33838-0_22Suche in Google Scholar
3. Cardona, G, Quevedo, N. Blinking and driving: the influence of saccades and cognitive workload. Curr Eye Res 2014;39:239–44. https://doi.org/10.3109/02713683.2013.841256.Suche in Google Scholar PubMed
4. Fallahi, M, Motamedzade, M, Heidarimoghadam, R, Soltanian, AR, Miyake, S. Assessment of operators’ mental workload using physiological and subjective measures in cement, city traffic and power plant control centers. Health Promot Perspect 2016;6:96. https://doi.org/10.15171/hpp.2016.17.Suche in Google Scholar PubMed PubMed Central
5. Lean, Y, Shan, F. Brief review on physiological and biochemical evaluations of human mental workload. Hum Factors Ergon Manuf Serv Ind 2012;22:177–87. https://doi.org/10.1002/hfm.20269.Suche in Google Scholar
6. Calderon, J, Yang, Y, Inman, C, Willie, J, Berman, G. Decoding human behavior from complex neural interactions. APS 2018;2018:S06–006.Suche in Google Scholar
7. Vatansever, D, Menon, DK, Manktelow, AE, Sahakian, BJ, Stamatakis, EA. Default mode dynamics for global functional integration. J Neurosci 2015;35:15254–62. https://doi.org/10.1523/jneurosci.2135-15.2015.Suche in Google Scholar PubMed PubMed Central
8. Gogna, Y, Singla, R, Tiwari, S. Steady state detection during A cognitive task. In: 2019 IEEE 5th international conference for convergence in technology (I2CT): IEEE; 2019:1–4 pp.10.1109/I2CT45611.2019.9033870Suche in Google Scholar
9. Hart, SG, Staveland, LE. Development of NASA-TLX (task load index): results of empirical and theoretical research. In: Hancock, PA, Meshkati NBT-A in, P, editors. Human mental workload [Internet]: North-Holland; 1988:139–83:pp. Available from: http://www.sciencedirect.com/science/article/pii/S0166411508623869.10.1016/S0166-4115(08)62386-9Suche in Google Scholar
10. Zijlstra, FRH. Efficiency in work behaviour: A design approach for modern tools. The Netherlands: Delft University Press; 1993.Suche in Google Scholar
11. Reid, GB, Nygren, TE. The subjective workload assessment technique: a scaling procedure for measuring mental workload. In: Hancock, PA, Meshkati NBT-A in, P, editors. Human mental workload [Internet]: North-Holland; 1988:185–218:pp. Available from: http://www.sciencedirect.com/science/article/pii/S0166411508623870.10.1016/S0166-4115(08)62387-0Suche in Google Scholar
12. Rogers, RD, Monsell, S. Costs of a predictible switch between simple cognitive tasks. J Exp Psychol Gen 1995;124:207. https://doi.org/10.1037/0096-3445.124.2.207.Suche in Google Scholar
13. Charles, RL, Nixon, J. Measuring mental workload using physiological measures: a systematic review. Appl Ergon 2019;74:221–32. https://doi.org/10.1016/j.apergo.2018.08.028.Suche in Google Scholar PubMed
14. Hussain, I, Park, SJ. HealthSOS: real-time health monitoring system for stroke prognostics. IEEE Access 2020;8:213574–86. https://doi.org/10.1109/access.2020.3040437.Suche in Google Scholar
15. Rathi, N, Singla, R, Tiwari, S. Brain signatures perspective for high-security authentication. Biomed Eng Appl Basis Commun 2020;32:2050025. https://doi.org/10.4015/s1016237220500258.Suche in Google Scholar
16. Hussain, I, Young, S, Park, SJ. Driving-induced neurological biomarkers in an advanced driver-assistance system. Sensors 2021;21:6985. https://doi.org/10.3390/s21216985.Suche in Google Scholar PubMed PubMed Central
17. Hussain, I, Hossain, MA, Jany, R, Bari, MA, Uddin, M, Kamal, AR, et al.. Quantitative evaluation of EEG-biomarkers for prediction of sleep stages. Sensors 2022;22:3079. https://doi.org/10.3390/s22083079.Suche in Google Scholar PubMed PubMed Central
18. Andreessen, LM, Gerjets, P, Meurers, D, Zander, TO. Toward neuroadaptive support technologies for improving digital reading: a passive BCI-based assessment of mental workload imposed by text difficulty and presentation speed during reading. User Model User-adapt Interact. 2020;31:1–30.10.1007/s11257-020-09273-5Suche in Google Scholar
19. Dehais, F, Duprès, A, Blum, S, Drougard, N, Scannella, S, Roy, RN, et al.. Monitoring pilot’s mental workload using ERPs and spectral power with a six-dry-electrode EEG system in real flight conditions. Sensors 2019;19:1324. https://doi.org/10.3390/s19061324.Suche in Google Scholar PubMed PubMed Central
20. Morton, J, Vanneste, P, Larmuseau, C, Van Acker, B, Raes, A, Bombeke, K, et al.. Identifying predictive EEG features for cognitive overload detection in assembly workers in Industry 4.0. In: 3rd international symposium on human mental workload: models and applications (H-WORKLOAD 2019). Ghent, Belgium: Ghent University; 2019.Suche in Google Scholar
21. Nicolas-Alonso, LF, Gomez-Gil, J. Brain computer interfaces, a review. Sensors 2012;12:1211–79. https://doi.org/10.3390/s120201211.Suche in Google Scholar PubMed PubMed Central
22. Grissmann, S, Spuler, M, Faller, J, Krumpe, T, Zander, T, Kelava, A, et al.. Context sensitivity of EEG-based workload classification under different affective valence. IEEE Trans Affect Comput 2017;11:327–34.10.1109/TAFFC.2017.2775616Suche in Google Scholar
23. Yin, Z, Zhang, J. Cross-session classification of mental workload levels using EEG and an adaptive deep learning model. Biomed Signal Process Control 2017;33:30–47. https://doi.org/10.1016/j.bspc.2016.11.013.Suche in Google Scholar
24. Ramírez-Moreno, MA, Díaz-Padilla, M, Valenzuela-Gómez, KD, Vargas-Martínez, A, Tudón-Martínez, JC, Morales-Menendez, R, et al.. Eeg-based tool for prediction of university students’ cognitive performance in the classroom. Brain Sci 2021;11:698. https://doi.org/10.3390/brainsci11060698.Suche in Google Scholar PubMed PubMed Central
25. Shuggi, IM, Oh, H, Shewokis, PA, Gentili, RJ. Mental workload and motor performance dynamics during practice of reaching movements under various levels of task difficulty. Neuroscience 2017;360:166–79. https://doi.org/10.1016/j.neuroscience.2017.07.048.Suche in Google Scholar PubMed
26. Hart, SG. NASA task load index (TLX). Moffett Field, CA. United States: NASA Ames Research Center; 1986.Suche in Google Scholar
27. Ille, N, Berg, P, Scherg, M. Artifact correction of the ongoing EEG using spatial filters based on artifact and brain signal topographies. J Clin Neurophysiol 2002;19:113–24. https://doi.org/10.1097/00004691-200203000-00002.Suche in Google Scholar PubMed
28. Jenke, R, Peer, A, Buss, M. Feature extraction and selection for emotion recognition from EEG. IEEE Trans Affect Comput 2014;5:327–39. https://doi.org/10.1109/taffc.2014.2339834.Suche in Google Scholar
29. Ahirwal, M, Londhe, N. Power spectrum analysis of EEG signals for estimating visual attention. Int J Comput Appl 2012;42:34–40. https://doi.org/10.5120/5769-7993.Suche in Google Scholar
30. Hussain, I, Young, S, Kim, CH, Benjamin, HC, Park, SJ. Quantifying physiological biomarkers of a microwave brain stimulation device. Sensors 2021;21:1896. https://doi.org/10.3390/s21051896.Suche in Google Scholar PubMed PubMed Central
31. Gogna, Y, Singla, R, Tiwari, S. Analyzing attention deviation during collaterally proceeding cognitive tasks. In: International congress on information and communication technology: Springer; 2020:490–7 pp.10.1007/978-981-15-5856-6_48Suche in Google Scholar
32. Sleigh, JW, Donovan, J. Comparison of bispectral index, 95% spectral edge frequency and approximate entropy of the EEG, with changes in heart rate variability during induction of general anaesthesia. Br J Anaesth 1999;82:666–71. https://doi.org/10.1093/bja/82.5.666.Suche in Google Scholar PubMed
33. Inouye, T, Shinosaki, K, Sakamoto, H, Toi, S, Ukai, S, Iyama, A, et al.. Quantification of EEG irregularity by use of the entropy of the power spectrum. Electroencephalogr Clin Neurophysiol 1991;79:204–10. https://doi.org/10.1016/0013-4694(91)90138-t.Suche in Google Scholar PubMed
34. Antonenko, P, Paas, F, Grabner, R, van Gog, T. Using electroencephalography to measure cognitive load. Educ Psychol Rev 2010;22:425–38. https://doi.org/10.1007/s10648-010-9130-y.Suche in Google Scholar
35. Wang, XW, Nie, D, Lu, BL. Emotional state classification from EEG data using machine learning approach. Neurocomputing 2014;129:94–106. https://doi.org/10.1016/j.neucom.2013.06.046.Suche in Google Scholar
36. Bashivan, P, Yeasin, M, Bidelman, GM. Single trial prediction of normal and excessive cognitive load through EEG feature fusion. In: 2015 IEEE signal processing in medicine and biology symposium (SPMB): IEEE; 2015:1–5 pp.10.1109/SPMB.2015.7405422Suche in Google Scholar
37. Kumar, H, Ganapathy, N, Puthankattil, SD, Swaminathan, R. EEG based emotion recognition using entropy features and Bayesian optimized random forest. Curr Dir Biomed Eng 2021;7:767–70. https://doi.org/10.1515/cdbme-2021-2196.Suche in Google Scholar
38. Radhakrishnan, M, Won, D, Manoharan, TA, Venkatachalam, V, Chavan, RM, Nalla, HD. Investigating electroencephalography signals of autism spectrum disorder (ASD) using higuchi fractal dimension. Biomed Eng/Biomed Tech 2021;66:59–70. https://doi.org/10.1515/bmt-2019-0313.Suche in Google Scholar PubMed
39. Rathi, N, Singla, R, Tiwari, S. Authentication framework for security application developed using a pictorial P300 speller. Brain Comput Interfaces 2020;7:70–89. https://doi.org/10.1080/2326263X.2020.1860520.Suche in Google Scholar
40. Schlögl, A, Lee, F, Bischof, H, Pfurtscheller, G. Characterization of four-class motor imagery EEG data for the BCI-competition 2005. J Neural Eng 2005;2:L14. https://doi.org/10.1088/1741-2560/2/4/l02.Suche in Google Scholar
41. Shi, M, Wang, C, Li, XZ, Li, MQ, Wang, L, Xie, NG. EEG signal classification based on SVM with improved squirrel search algorithm. Biomed Eng/Biomed Tech 2021;66:137–52. https://doi.org/10.1515/bmt-2020-0038.Suche in Google Scholar PubMed
42. Zammouri, A, Moussa, AA, Mebrouk, Y. Brain-computer interface for workload estimation: assessment of mental efforts in learning processes. Expert Syst Appl 2018;112:138–47. https://doi.org/10.1016/j.eswa.2018.06.027.Suche in Google Scholar
43. Hussain, I, Park, SJ. Quantitative evaluation of task-induced neurological outcome after stroke. Brain Sci 2021;11:900. https://doi.org/10.3390/brainsci11070900.Suche in Google Scholar PubMed PubMed Central
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Artikel in diesem Heft
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Artikel in diesem Heft
- Frontmatter
- Review
- A review: strategies to reduce infection in tantalum and its derivative applied to implants
- Research Articles
- Biomechanical analysis of different fixed dental restorations on short implants: a finite element study
- Highly sensitive temperature sensor using one-dimensional Bragg Reflector for biomedical applications
- Synchronisation of wearable inertial measurement units based on magnetometer data
- Multiple ECG segments denoising autoencoder model
- Heart sound classification based on equal scale frequency cepstral coefficients and deep learning
- Towards a versatile mental workload modeling using neurometric indices
- An improved multi-source domain adaptation network for inter-subject mental fatigue detection based on DANN
