Abstract
Purpose of this study was to understand the effect of automatic muscle pruning of electroencephalograph on cognitive work load prediction. Pruning was achieved using an automatic Independent Component Analysis (ICA) based component classification. Initially, raw data from EEG recording was used for prediction, this result was then compared with mental work load prediction results from muscle-pruned EEG data. This study used Support Vector Machine (SVM) with Linear Kernel for cognitive work load prediction from EEG data. Initial part of the study was to learn a classification model from the whole data, whereas the second part was to learn the model from a set of subjects and predict the mental work load for an unseen subject by the model. The experimental results show that an accuracy of nearly 100 % is possible with ICA and automatic pruning based pre-processing. Cross subject prediction significantly improved from a mean accuracy of 54 % to 69 % for an unseen subject with the pre-processing.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Ball, T., Kern, M., Mutschler, I., Aertsen, A., Schulze-Bonhage, A.: Signal quality of simultaneously recorded invasive and non-invasive EEG. NeuroImage 46(3), 708–716 (2009)
Blankertz, B., Tomioka, R., Lemm, S., Kawanabe, M., Muller, K.R.: Optimizing spatial filters for robust EEG single-trial analysis. IEEE Sig. Process. Mag. 25(1), 41–56 (2008)
Delorme, A., Palmer, J., Onton, J., Oostenveld, R., Makeig, S.: Independent EEG sources are dipolar. PloS ONE 7(2), e30135 (2012)
Domen, N., Mihelj, M., Marko, M.: A survey of methods for data fusion and system adaptation using autonomic nervous system responses in physiological computing. interact. Comput. 24(3), 154–172 (2012)
Fitzgibbon, S., DeLosAngeles, D., Lewis, T., Powers, D., Grummett, T., Whitham, E., Ward, L., Willoughby, J., Pope, K.: Automatic determination of EMG-contaminated components and validation of independent component analysis using EEG during pharmacologic paralysis. Clin. Neurophysiol. 127, 1781–1793 (2015)
Gevins, A., Smith, M.E.: Neurophysiological measures of cognitive workload during human-computer interaction. Theor. Issues Ergon. Sci. 4(1–2), 113–131 (2003)
Grummett, T.S., Fitzgibbon, S.P., Lewis, T.W., et al.: Constitutive spectral EEG peaks in the gamma range: suppressed by sleep, reduced by mental activity and resistant to sensory stimulation. Front. Hum. Neurosci. 8(927) (2014)
Heger, D., Putze, F., Schultz, T.: Online workload recognition from EEG data during cognitive tests and human-machine interaction. In: Dillmann, R., Beyerer, J., Hanebeck, U.D., Schultz, T. (eds.) KI 2010. LNCS (LNAI), vol. 6359, pp. 410–417. Springer, Heidelberg (2010). doi:10.1007/978-3-642-16111-7_47
Hsu, C.W., Chang, C.C., Lin, C.J., et al.: A practical guide to support vector classification (2003)
Kohlmorgen, J., Dornhege, G., Braun, M.L., Blankertz, B., Müller, K.R., Curio, G., Hagemann, K., Bruns, A., Schrauf, M., Kincses, W.E.: Improving human performance in a real operating environment through real-time mental workload detection. In: Towards Brain-Computer Interfacing. The MIT press (2006)
Kothe, C.A., Makeig, S.: Bcilab: a platform for brain-computer interface development. J. Neural Eng. 10(5), 056014 (2013)
Kubler, A., Mattia, D.: Chapter 14 - Brain computer interface based solutions for end-users with severe communication disorders. In: Laureys, S., Gosseries, O., Tononi, G. (eds.) The Neurology of Conciousness, 2nd edn, pp. 217–240. Academic Press, San Diego (2016)
Niedermeyer, E., da Silva, F.H.L.: Electroencephalography: Basic Principles, Clinical Applications and Related Fields. Williams and Wilkins, Lippincott, Philadelphia (1993)
Putze, F., Jarvis, J.P., Schultz, T.: Multimodal recognition of cognitive workload for multitasking in the car. In: 2010 20th International Conference on Pattern Recognition (ICPR), pp. 3748–3751 (2010)
Samuel, A.L.: Some studies in machine learning using the game of checkers. IBM J. Res. Develop. 3, 210–229 (1959)
Teplan, M.: Fundamentals of EEG measurement. Measur. Sci. Rev. 2(2), 1–11 (2002)
Wang, Z., Hope, R.M., Wang, Z., Ji, Q., Gray, W.D.: Cross-subject workload classification with a hierarchical Bayes model. NeuroImage 59(1), 64–69 (2012)
Welch, P.D.: The use of fast fourier transform for the estimation of power spectra: a method based on time averaging over short, modified periodograms. IEEE Trans. Audio Electroacoust. 15(2), 70–73 (1967)
Whitham, E.M., Pope, K.J., Fitzgibbon, S.P., Lewis, T., Clark, C.R., Loveless, S., Broberg, M., Wallace, A., DeLosAngeles, D., Lillie, P., et al.: Scalp electrical recording during paralysis: quantitative evidence that EEG frequencies above 20Hz are contaminated by EMG. Clin. Neurophysiol. 118(8), 1877–1888 (2007)
Wilson, G.F.: An analysis of mental workload in pilots during flight using multiple psychophysiological measures. Int. J. Aviat. Psychol. 12(1), 3–18 (2002)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing AG
About this paper
Cite this paper
Kunjan, S. et al. (2016). Cross Subject Mental Work Load Classification from Electroencephalographic Signals with Automatic Artifact Rejection and Muscle Pruning. In: Ascoli, G., Hawrylycz, M., Ali, H., Khazanchi, D., Shi, Y. (eds) Brain Informatics and Health. BIH 2016. Lecture Notes in Computer Science(), vol 9919. Springer, Cham. https://doi.org/10.1007/978-3-319-47103-7_29
Download citation
DOI: https://doi.org/10.1007/978-3-319-47103-7_29
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-47102-0
Online ISBN: 978-3-319-47103-7
eBook Packages: Computer ScienceComputer Science (R0)