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Classification Mental Workload Levels from EEG Signals with 1D Convolutional Neural Network

Recep Baydemir1✉,
Fatma Latifoğlu2,
Fırat Orhanbulucu3
1Erciyes University
2Erciyes University
3Inonu University
Received:Nov 16, 2022→Accepted:Dec 24, 2022→Published:December 31, 2022
DOI: 10.56038/ejrnd.v2i4.193
Vol. 2, No. 4
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A 1D convolutional neural network successfully classified mental workload levels from EEG signals with high accuracy.

Researchers used a one-dimensional convolutional neural network to classify mental workload levels from EEG signals. They achieved high accuracy rates, including 98.4% overall accuracy, 97.62% sensitivity, and 98.94% specificity. The study involved two stages of classification, with and without Empirical Mode Decomposition.

Abstract

Mental workload (MWL) can be estimated according to the state of cognitive capacity after an activity. In this study, it is aimed to classify MWL levels from Electroencephalogram (EEG) signals recorded from a task moment. Using the proposed one-dimensional convolutional neural network (1D-CNN) model in the study, low (L) and high (H) level WL states were classified. The classification process was carried out in two stages. EEG signals passed through the preprocessing stage were classified with 1D-CNN in the first stage. In the second step, these signals were decomposed into subbands by applying Empirical Mode Decomposition (EMD) and classified with 1D-CNN. As a result of the classification process, accuracy (Acc), sensitivity (Sens), and specificity (Spe) values were obtained and evaluated in this study. As a result of the evaluation, the most successful Acc rate was 98.4%, Sens rate 97.62%, and Spe rate 98.94%

Keywords
ElectroencephalogramMental WorkloadConvolutional Neural Network

References

  1. 1.Young, M. S., Brookhuis, K. A., Wickens, C. D., & Hancock, P. A. (2015). State of science: mental workload in ergonomics. Ergonomics, 58(1), 1-17.
  2. 2.Tao, D., Tan, H., Wang, H., Zhang, X., Qu, X., & Zhang, T. (2019). A systematic review of physiological measures of mental workload. International journal of environmental research and public health, 16(15), 2716.
  3. 3.Kothe, C. A., & Makeig, S. (2011, September). Estimation of task workload from EEG data: new and current tools and perspectives. In 2011 annual international conference of the IEEE engineering in medicine and biology society (pp. 6547-6551). IEEE.
  4. 4.Marinescu, A., Sharples, S., Ritchie, A. C., López, T. S., McDowell, M., & Morvan, H. (2016). Exploring the relationship between mental workload, variation in performance and physiological parameters. IFAC-PapersOnLine, 49(19), 591-596.
  5. 5.Duru, A. D. (2019). Determination of increased mental workload condition from EEG by the use of classification techniques. International Journal of Advances in Engineering and Pure Sciences, 31(1), 47-52.
  6. 6.Amin, H. U., Malik, A. S., Ahmad, R. F., Badruddin, N., Kamel, N., Hussain, M., & Chooi, W. T. (2015). Feature extraction and classification for EEG signals using wavelet transform and machine learning techniques. Australasian physical & engineering sciences in medicine, 38(1), 139-149.
  7. 7.Qu, H., Shan, Y., Liu, Y., Pang, L., Fan, Z., Zhang, J., & Wanyan, X. (2020). Mental workload classification method based on EEG independent component features. Applied Sciences, 10(9), 3036.
  8. 8.Qu, H., Zhang, M., & Pang, L. (2022). Mental Workload Classification Method Based on EEG Cross-Session Subspace Alignment. Mathematics, 10(11), 1875.
  9. 9.AYDIN, E. A. (2021). EEG sinyalleri kullanılarak zihinsel iş yükü seviyelerinin sınıflandırılması. Politeknik Dergisi, 24(2), 681-689.
  10. 10.Singh, U., & Ahirwal, M. K. (2021, December). Mental Workload Classification for Multitasking Test using Electroencephalogram Signal. In 2021 IEEE International Conference on Technology, Research, and Innovation for Betterment of Society (TRIBES) (pp. 1-6). IEEE.
  11. 11.Jiménez-Guarneros, M., & Gómez-Gil, P. (2020). Custom Domain Adaptation: A new method for cross-subject, EEG-based cognitive load recognition. IEEE Signal Processing Letters, 27, 750-754.
  12. 12.Saini, M., Satija, U., & Upadhayay, M. D. (2022). One-dimensional convolutional neural network architecture for classification of mental tasks from electroencephalogram. Biomedical Signal Processing and Control, 74, 103494.
  13. 13.Lim, W. L., Sourina, O., & Wang, L. P. (2018). STEW: Simultaneous task EEG workload data set. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 26(11), 2106-2114. Accessed: Mar. 18, 2021. [Online]. Available: https://ieee-dataport.org/open-access/stew-simultaneous-task-eeg-workload-datasetLink
  14. 14.Bratfisch, O., & Hagman, E. (2008). SIMKAP–Simultankapazität/Multi-Tasking. Mödling: Schuhfried GmbH.
  15. 15.Huang, N. E., Shen, Z., Long, S. R., Wu, M. C., Shih, H. H., Zheng, Q., ... & Liu, H. H. (1998). The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis. Proceedings of the Royal Society of London. Series A: mathematical, physical and engineering sciences, 454(1971), 903-995.
  16. 16.Pachori, R. B., & Bajaj, V. (2011). Analysis of normal and epileptic seizure EEG signals using empirical mode decomposition. Computer methods and programs in biomedicine, 104(3), 373-381.
  17. 17.Janssens, O., Slavkovikj, V., Vervisch, B., Stockman, K., Loccufier, M., Verstockt, S., ... & Van Hoecke, S. (2016). Convolutional neural network based fault detection for rotating machinery. Journal of Sound and Vibration, 377, 331-345.
  18. 18.Croce, P., Zappasodi, F., Marzetti, L., Merla, A., Pizzella, V., & Chiarelli, A. M. (2018). Deep Convolutional Neural Networks for feature-less automatic classification of Independent Components in multi-channel electrophysiological brain recordings. IEEE Transactions on Biomedical Engineering, 66(8), 2372-2380.
  19. 19.Kingphai, K., & Moshfeghi, Y. (2022, September). On time series cross-validation for deep learning classification model of mental workload levels based on EEG signals. In Advanced Online & Onsite Course & Symposium on Artificial Intelligence & Neuroscience.
  20. 20.Chakladar, D. D., Dey, S., Roy, P. P., & Dogra, D. P. (2020). EEG-based mental workload estimation using deep BLSTM-LSTM network and evolutionary algorithm. Biomedical Signal Processing and Control, 60, 101989.
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Cite This Article
Baydemir, R., Latifoğlu, F., Orhanbulucu, F. (2022). Classification Mental Workload Levels from EEG Signals with 1D Convolutional Neural Network. *The European Journal of Research and Development*, 2(4), 13-23. https://doi.org/10.56038/ejrnd.v2i4.193

Bibliographic Info

JournalThe European Journal of Research and Development
Volume2
Issue4
Pages13–23
PublishedDecember 31, 2022
eISSN2822-2296

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Open AccessCC BY 4.0CrossRef DOIORCIDOAI-PMH

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