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Spectral EEG-based classification for operator dyads' workload and cooperation level estimation

Abstract : There is a growing momentum to design online tools to measure mental workload for neuroergonomic purposes. Most of the research focuses on the monitoring of a single human operator. However, in real-life situations, human operators work in cooperation to optimize safety and performance. This is particularly the case in aviation whereby crews are composed of a pilot flying and a pilot monitoring. The motivation of this study is to evaluate the possibility to apply an hyperscanning approach to estimate the mental workload of crews composed of two operators. We designed an experimental protocol in which ten crews (i.e. 20 subjects) had to perform a modified version of the NASA MATBII during 8 five-minute blocks (i.e. 4 mental workload level configurations * 2 cooperation v. non cooperation conditions). Mental workload and cooperation level were classified using a traditional passive brain-computer interface pipeline that includes a spatial filtering step on frequency features. Our results disclosed that all mental states’ estimations were significantly above chance level. Intra-subject classification accuracy for mental workload (2 classes) was 63% for the pilot flying and 58% for the pilot monitoring. As for cooperation level, the binary classification reached 57% for the pilot flying and 60% for the pilot monitoring. Regarding the team, intra-team classification accuracy of the workload configuration of the team (4-class) reached 35%. As for the team cooperation level, the binary classifier reached 60% of accuracy. The results are discussed in terms of hyperscanning applications.
Keywords : Hyperscanning BCI EEG MATB
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Submitted on : Wednesday, November 6, 2019 - 3:26:36 PM
Last modification on : Monday, September 7, 2020 - 3:04:03 PM
Long-term archiving on: : Saturday, February 8, 2020 - 1:14:01 AM


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  • HAL Id : hal-02351808, version 1
  • OATAO : 24552


Kevin J. Verdière, Frédéric Dehais, Raphaëlle N. Roy. Spectral EEG-based classification for operator dyads' workload and cooperation level estimation. IEEE SMC 2019, Oct 2019, Bari, Italy. pp.3915-3920. ⟨hal-02351808⟩



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