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Neurofeedback for Personalized Adaptive Training

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Advances in Neuroergonomics and Cognitive Engineering (AHFE 2017)

Abstract

Learning of complex skills can be enhanced if the training can adapt to the learner. Earlier studies utilized behavioral performance based adaptation; however, behavioral performance assessment is limited and does not take into account the mental effort and the brain plasticity changes during the acquisition of complex skills. In this study, we utilized objective brain based measures for the assessment and adaptation of a four-day training program with three piloting tasks on four participants using a low fidelity flight simulator. Functional near-infrared spectroscopy (fNIRS) from prefrontal cortex was measured to adapt the difficulty levels of the training trials of the tasks with the aim of optimizing the cognitive workload. Participants also performed reference practice trials that had the same difficulty across sessions. Preliminary results identified specific brain areas within prefrontal cortex for each reference tasks that corroborates earlier task practice studies. Furthermore, the same brain areas were responsive to the adaptive training trials as well. The results overall suggest task specific brain areas are coupled with the behavioral performance. This study outlines a new neurofeedback based training paradigm using the wearable and portable fNIRS. Future uses of such personalized training could help prevent unnecessary over-training or insufficient under-training.

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Acknowledgments

This investigation was in part funded by Lockheed Martin. The views, opinions, and/or findings contained in this article are those of the authors and should not be interpreted as representing the official views or policies, either expressed or implied, of the funding agency.

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Correspondence to Hasan Ayaz .

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Mark, J., Thomas, N., Kraft, A., Casebeer, W.D., Ziegler, M., Ayaz, H. (2018). Neurofeedback for Personalized Adaptive Training. In: Baldwin, C. (eds) Advances in Neuroergonomics and Cognitive Engineering. AHFE 2017. Advances in Intelligent Systems and Computing, vol 586. Springer, Cham. https://doi.org/10.1007/978-3-319-60642-2_8

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  • DOI: https://doi.org/10.1007/978-3-319-60642-2_8

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