Abstract
For cognitive workload recognition, electroencephalography (EEG) signals vary from different subjects, thus hindering the recognition performance when direct extending to a new subject. Though calibrating the new subject or collecting more data would alleviate this issue, it is generally time-consuming and unrealistic. To cope with the problem, we propose a deep domain adaptation scheme for EEG-based cross-subject cognitive workload recognition, using the knowledge from the existing subjects (source domain) to improve the recognition performance of a new subject (target domain). Specifically, the proposed method has four modules: the EEG features extractor, feature distribution alignment, label classifier, and domain discriminator. The EEG feature extractor learns transferable shallow feature representation of both domains. The label classifier further learns the deep representation from the shallow one and trains the classifier. To reduce the domain discrepancy, we employ feature distribution alignment and domain discriminator from shallow and deep representation views using a distribution discrepancy metric and adversarial training with the feature extractor, respectively. We conduct experiments to recognize the low and high workload levels on a self-designed EEG dataset with 38 subjects performing the working memory cognitive task. Experimental results validate that our proposed framework outperforms the baselines significantly.
Supported by the National Natural Science Foundation of China under Grant 62136004, Grant 61876082, and Grant 61732006; the National Key Research and Development Program of China under Grant 2018YFC2001600 and Grant 2018YFC2001602; the Fundamental Research Funds for the Central Universities under Grant NP2022451.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Appel, T., et al.: Cross-task and cross-participant classification of cognitive load in an emergency simulation game. IEEE Trans. Affect. Comput. (2021)
Wickens, C.D.: Multiple resources and performance prediction. Theor. Issues Ergon. Sci. 3(2), 159–177 (2002)
Jimenez-Molina, A., Retamal, C., Lira, H.: Using psychophysiological sensors to assess mental workload during web browsing. Sensors 18(2), 458 (2018)
Almogbel, M.A., Dang, A.H., Kameyama, W.: Cognitive workload detection from raw eeg-signals of vehicle driver using deep learning. In: 2019 21st International Conference on Advanced Communication Technology (ICACT), pp. 1–6. IEEE (2019)
Dehais, F., et al.: Monitoring pilot’s mental workload using erps and spectral power with a sixdry-electrode eeg system in real flight conditions. Sensors 19(6), 1324 (2019)
Zhou, Y., Huang, S., Xu, Z., Wang, P., Wu, X., Zhang, D.: Cognitive workload recognition using EEG signals and machine learning: A review. IEEE Trans. Cogn. Dev. Syst. 14(3), 799–818 (2022)
Tao, D., Tan, H., Wang, H., Zhang, X., Qu, X., Zhang, T.: A systematic review of physiological measures of mental workload. Int. J. Environ. Res. Public Health 16(15), 2716 (2019)
Charles, R.L., Nixon, J.: Measuring mental workload using physiological measures: a systematic review. Appl. Ergon. 74, 221–232 (2019)
Chikhi, S., Matton, N., Blanchet, S.: EEG power spectral measures of cognitive workload: a meta-analysis. Psychophysiology, e14009 (2022)
Gu, X., et al.: Eeg-based brain computer interfaces (bcis): a survey of recent studies on signal sensing technologies and computational intelligence approaches and their applications. IEEE/ACM Trans. Comput. Biol. Bioinf. 18, 1645–1666 (2021)
Kakkos, I., et al.: EEG fingerprints of task-independent mental workload discrimination. IEEE J. Biomed. Health Inf. 25, 3824–3833 (2021)
Gupta, A., Siddhad, G., Pandey, V., Roy, P.P., Kim, B.G.: Subject-specific cognitive workload classification using eeg-based functional connectivity and deep learning. Sensors (Basel, Switzerland) 21 (2021)
Pang, L., Guo, L., Zhang, J., Wanyan, X., Qu, H., Wang, X.: Subject-specific mental workload classification using eeg and stochastic configuration network (scn). Biomed. Signal Process. Control. 68, 102711 (2021)
Li, W., Huan, W., Hou, B., Tian, Y., Zhang, Z., Song, A.: Can emotion be transferred? - a review on transfer learning for eeg-based emotion recognition. IEEE Trans. Cogn. Dev. Syst. 14, 833–846 (2021)
Bhosale, S., Chakraborty, R., Kopparapu, S.K.: Calibration free meta learning based approach for subject independent eeg emotion recognition. Biomed. Signal Process. Control 72, 103289 (2022)
Wang, Z., Hope, R.M., Wang, Z., Ji, Q., Gray, W.D.: Cross-subject workload classification with a hierarchical bayes model. NeuroImage 59, 64–69 (2012)
Plechawska-Wojcik, M., Tokovarov, M., Kaczorowska, M., la Zapa, D.: A three-class classification of cognitive workload based on eeg spectral data. Appl. Sci. 9, 5340 (2019)
Yin, Z., Zhao, M., Zhang, W., Wang, Y., Wang, Y., Zhang, J.: Physiological-signalbased mental workload estimation via transfer dynamical autoencoders in a deep learning framework. Neurocomputing 347, 212–229 (2019)
Hefron, R.G., Borghetti, B.J., Schubert-Kabban, C.M., Christensen, J.C., Estepp, J.R.: Cross-participant eeg-based assessment of cognitive workload using multipath convolutional recurrent neural networks. Sensors (Basel, Switzerland) 18(2018)
Ni, Z., Xu, J., Wu, Y., Li, M., Xu, G., Xu, B.: Improving cross-state and crosssubject visual erp-based bci with temporal modeling and adversarial training. IEEE Trans. Neural Syst. Rehab. Eng. 30, 369–379 (2022)
Borgwardt, K.M., Gretton, A., Rasch, M.J., Kriegel, H.P., Scholkopf, B., Smola, A.: Integrating structured biological data by kernel maximum mean discrepancy. Bioinformatics 22(14), e49–57 (2006)
Wang, W., et al.: Rethinking maximum mean discrepancy for visual domain adaptation. IEEE Trans. Neural Netw. Learn. Syst. 34, 264–277 (2021)
Ajakan, H., Germain, P., Larochelle, H., Laviolette, F., Marchand, M.: Domainadversarial neural networks. arXiv preprint arXiv:1412.4446 (2014)
Zhao, H., Zheng, Q., Ma, K., Li, H., Zheng, Y.: Deep representation-based domain adaptation for nonstationary eeg classification. IEEE Trans. Neural Netw. Learn. Syst. 32, 535–545 (2021)
Ganin, Y., et al.: Domain-adversarial training of neural networks. J. Mach. Learn. Res. 17, 2030–2096 (2016)
Zhou, Y., et al.: Cross-task cognitive workload recognition based on eeg and domain adaptation. IEEE Trans. Neural Syst. Rehabil. Eng. 30, 50–60 (2022)
Roy, R.N., Charbonnier, S., Campagne, A., Bonnet, S.: Efficient mental workload estimation using task-independent eeg features. J. Neural Eng. 13(2), 026019 (2016)
Spape, M.M.A., Verdonschot, R.G., van Danzig, S., van Steenbergen, H.: The eprimer: an introduction to creating psychological experiments in e-prime (2014)
Hefron, R.G., Borghetti, B.J., Christensen, J.C., Schubert-Kabban, C.M.: Deep long short-term memory structures model temporal dependencies improving cognitive workload estimation. Pattern Recogn. Lett. 94, 96–104 (2017)
Lawhern, V.J., Solon, A.J., Waytowich, N.R., Gordon, S.M., Hung, C.P., Lance, B.: Eegnet: a compact convolutional network for eeg-based brain-computer interfaces. J. Neural Eng. 15, 056013 (2018)
Schirrmeister, R.T., Springenberg, J.T., Fiederer, L.D.J., Glasstetter, M., Eggensperger, K., Tangermann, M., Hutter, F., Burgard, W., Ball, T.: Deep learning with convolutional neural networks for eeg decoding and visualization. Human Brain Mapp. 38, 5391–5420 (2017)
Woolson, R.F.: Wilcoxon signed-rank test. In: Wiley Encyclopedia of Clinical Trials, pp. 1–3 (2007)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Zhou, Y. et al. (2023). Deep Domain Adaptation for EEG-Based Cross-Subject Cognitive Workload Recognition. In: Tanveer, M., Agarwal, S., Ozawa, S., Ekbal, A., Jatowt, A. (eds) Neural Information Processing. ICONIP 2022. Communications in Computer and Information Science, vol 1792. Springer, Singapore. https://doi.org/10.1007/978-981-99-1642-9_20
Download citation
DOI: https://doi.org/10.1007/978-981-99-1642-9_20
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-99-1641-2
Online ISBN: 978-981-99-1642-9
eBook Packages: Computer ScienceComputer Science (R0)