Abstract
As a non-invasive optical neuroimaging technique, functional near infrared spectroscopy (fNIRS) is currently used to assess brain dynamics during the performance of complex works and everyday tasks. However, the deep learning approaches to distinguish stress levels based on the changes of hemoglobin concentrations have not yet been extensively investigated. In this paper, we evaluated the efficiencies of advanced methods differentiating the rest and task periods during stroop task experiments. First, we explored that the apparent changes of oxy-hemoglobin (HbO) and deoxy-hemoglobin (HbR) concentrations associated with two mental stages did exist across each participant. Then, a novel discrimination framework was studied. Deep learning approaches, including convolutional neural network (CNN), deep belief networks (DBN), have enabled better classification accuracies of 84.26 ± 9.10% and 65.43 ± 1.59% as our preliminary study.
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Acknowledgements
This work was supported by the Basic Science Research Program through the NRF funded by the Ministry of Education (NRF-2017R1D1A1B03036423) and the Brain Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science, ICT & Future Planning (NRF-2016M3C7A1905477, NRF-2014M3C7A1046050). All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards. This article does not contain any studies with animals performed by any of the authors.
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Ho, T.K.K., Gwak, J., Park, C.M., Khare, A., Song, JI. (2019). Deep Leaning-Based Approach for Mental Workload Discrimination from Multi-channel fNIRS. In: Khare, A., Tiwary, U., Sethi, I., Singh, N. (eds) Recent Trends in Communication, Computing, and Electronics. Lecture Notes in Electrical Engineering, vol 524. Springer, Singapore. https://doi.org/10.1007/978-981-13-2685-1_41
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DOI: https://doi.org/10.1007/978-981-13-2685-1_41
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