@article{poussot-vassal_charles_roy_bovo_angela_gateau_dehais_carvalho_chanel_loewner_et al._2017, 
  title={A Loewner-based Approach for the Approximation of Engagement-related Neurophysiological Features}, 
  abstractNote={Currently, in order to increase both safety and performance of human-machine systems, researchers from various domains gather together to work towards the use of operators' mental state estimation in the systems control-loop. Mental state estimation is performed using neurophysiological data recorded, for instance, using electroencephalography (EEG). Features such as power spectral densities in specific frequency bands are extracted from these data and used as indices or metrics. Another interesting approach could be to identify the dynamic model of such features. Hence, this article discusses the potential use of tools derived from the linear algebra and control communities to perform an approximation of the neurophysiological features model that could be explored to monitor the engagement of an operator. The method provides a smooth interpolation of all the data points allowing to extract frequential features that reveal fluctuations in engagement with growing time-on-task.}, 
  author={Poussot-Vassal and Charles and Roy and Bovo and Angela and Gateau and Dehais and Carvalho and Chanel and Loewner and et al.}, 
  year={2017}
  }