Mental Workload Estimation from EEG Signals Using Machine Learning Algorithms

  • Baljeet Singh Cheema
  • Shabnam Samima
  • Monalisa Sarma
  • Debasis SamantaEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10906)


Multitasking conditions prevalent in many environments such as critical operations in defense activities, evaluating user interfaces in man-machine interaction, etc. require assessment of mental workload of operators. However, mental workload (MWL) cannot be perceived directly as it is a complex and abstract property of human physiology. The techniques available in the literature for its assessment usually depend on subjective analysis, performance analysis and psycho-physiological measurements. But, these approaches despite being followed often prove to be inadequate due to high inter-personal variations and inconvenient procedures and subjective to the bias of evaluators. With the recent advancements of proliferation of Brain Computer Interface (BCI) devices and machine learning algorithms, it is possible to estimate MWL automatically. Nevertheless, there is a need to address issue like managing a high dimension and high volume data in real time. In this work, we propose an approach to estimate the mental workload using electroencephalogram (EEG) signals of an operator while in operation. A thorough investigation of different features, optimization of features and selecting an optimal number of channels are the some of the crucial steps have been addressed in this work. We propose a novel feature engineering method to extract a reduced set of features and utilize only a sub-set of channels for the purpose of classification of workload into different levels with the help of supervised machine learning techniques. Further, we investigate the performance of different classifiers and compare their results. It can be inferred from the observed results that mental workload estimation using machine learning algorithms is a better solution compared to the existing approaches.


Electroencephalography Psycho-physiological measurement Brain-computer interface Mental workload estimation Machine learning algorithms n-back task Dual n-back task 


  1. 1.
    Anderson, E.W., Potter, K.C., Matzen, L.E., Shepherd, J.F., Preston, G.A., Silva, C.T.: A user study of visualization effectiveness using EEG and cognitive load. Comput. Graph. Forum 30(3), 791–800 (2011)CrossRefGoogle Scholar
  2. 2.
    Ayaz, H., Onaral, B., Izzetoglu, K., Shewokis, P.A., McKendrick, R., Parasuraman, R.: Continuous monitoring of brain dynamics with functional near infrared spectroscopy as a tool for neuroergonomic research: empirical examples and a technological development. Front. Human Neurosci. 7, 871 (2013)CrossRefGoogle Scholar
  3. 3.
    Benbadis, S.R.: EEG artifacts. Accessed 14 Feb 2018
  4. 4.
    Berka, C., Levendowski, D.J., Lumicao, M.N., Yau, A., Davis, G., Zivkovic, V.T., Olmstead, R.E., Tremoulet, P.D., Craven, P.L.: EEG correlates of task engagement and mental workload in vigilance, learning, and memory tasks. Aviat. Sp. Environ. Med. 78(5), B231–B244 (2007)Google Scholar
  5. 5.
    Chaouachi, M., Jraidi, I., Frasson, C.: Modeling mental workload using EEG features for intelligent systems. In: Konstan, J.A., Conejo, R., Marzo, J.L., Oliver, N. (eds.) UMAP 2011. LNCS, vol. 6787, pp. 50–61. Springer, Heidelberg (2011). Scholar
  6. 6.
    Daly, I., Scherer, R., Billinger, M., Müller-Putz, G.: Force: fully online and automated artifact removal for brain-computer interfacing. IEEE Trans. Neural Syst. Rehabil. Eng. 23(5), 725–736 (2015)CrossRefGoogle Scholar
  7. 7.
    Heine, T., Lenis, G., Reichensperger, P., Beran, T., Doessel, O., Deml, B.: Electrocardiographic features for the measurement of drivers’ mental workload. Appl. Ergon. 61, 31–43 (2017)CrossRefGoogle Scholar
  8. 8.
    Hirshfield, L.M., Chauncey, K., Gulotta, R., Girouard, A., Solovey, E.T., Jacob, R.J.K., Sassaroli, A., Fantini, S.: Combining electroencephalograph and functional near infrared spectroscopy to explore users’ mental workload. In: Schmorrow, D.D., Estabrooke, I.V., Grootjen, M. (eds.) FAC 2009. LNCS (LNAI), vol. 5638, pp. 239–247. Springer, Heidelberg (2009). Scholar
  9. 9.
    Holm, A., Lukander, K., Korpela, J., Sallinen, M., Müller, K.M.I.: Estimating brain load from the EEG. Sci. World J. 9, 639–651 (2009)CrossRefGoogle Scholar
  10. 10.
    Hoskinson, P.: Brain workshop - a dual n-back game. Accessed 14 Feb 2018
  11. 11.
    Ke, Y., Qi, H., He, F., Liu, S., Zhao, X., Zhou, P., Zhang, L., Ming, D.: An EEG-based mental workload estimator trained on working memory task can work well under simulated multi-attribute task. Front. Hum. Neurosci. 8, 703 (2014)CrossRefGoogle Scholar
  12. 12.
    Mahmoud, R., Shanableh, T., Bodala, I.P., Thakor, N., Al-Nashash, H.: Novel classification system for classifying cognitive workload levels under vague visual stimulation. IEEE Sens. J. 17, 7019–7028 (2017)CrossRefGoogle Scholar
  13. 13.
    Moustafa, K., Luz, S., Longo, L.: Assessment of mental workload: a comparison of machine learning methods and subjective assessment techniques. In: Longo, L., Leva, M.C. (eds.) H-WORKLOAD 2017. CCIS, vol. 726, pp. 30–50. Springer, Cham (2017). Scholar
  14. 14.
    Mühl, C., Jeunet, C., Lotte, F.: EEG-based workload estimation across affective contexts. Front. Neurosci. 8, 114 (2014)Google Scholar
  15. 15.
    Peng, H., Long, F., Ding, C.: Feature selection based on mutual information criteria of max-dependency, max-relevance, and min-redundancy. IEEE Trans. Pattern Anal. Mach. Intell. 27(8), 1226–1238 (2005)CrossRefGoogle Scholar
  16. 16.
    Roy, R.N., Bonnet, S., Charbonnier, S., Campagne, A.: Efficient workload classification based on ignored auditory probes: a proof of concept. Front. Hum. Neurosci. 10, 519 (2016)CrossRefGoogle Scholar
  17. 17.
    Samima, S., Sarma, M., Samanta, D.: Correlation of P300 ERPS with visual stimuli and its application to vigilance detection. IEEE, July 2017Google Scholar
  18. 18.
    Urigüen, J.A., Garcia-Zapirain, B.: EEG artifact removal-state-of-the-art and guidelines. J. Neural Eng. 12(3), 031001 (2015)CrossRefGoogle Scholar
  19. 19.
    Wang, S., Gwizdka, J., Chaovalitwongse, W.A.: Using wireless EEG signals to assess memory workload in the \(n\)-back task. IEEE Trans. Hum.-Mach. Syst. 46(3), 424–435 (2016)CrossRefGoogle Scholar
  20. 20.
    Zarjam, P., Epps, J., Chen, F., Lovell, N.H.: Estimating cognitive workload using wavelet entropy-based features during an arithmetic task. Comput. Biol. Med. 43(12), 2186–2195 (2013)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Baljeet Singh Cheema
    • 1
  • Shabnam Samima
    • 2
  • Monalisa Sarma
    • 2
  • Debasis Samanta
    • 3
    Email author
  1. 1.Directorate General of Information System, Integrated Headquarters, Ministry of Defence (Army) Government of IndiaKharagpurIndia
  2. 2.Subir Chowdhury School of Quality and ReliabilityIndian Institute of Technology KharagpurKharagpurIndia
  3. 3.Department of Computer Science and EngineeringIndian Institute of Technology KharagpurKharagpurIndia

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