Riemannian Geometry Applied to Detection of Respiratory States from EEG Signals: The Basis for a Brain-Ventilator Interface

X. Navarro-Sune, A. L. Hudson, F. De Vico Fallani, J. Martinerie, A. Witon, P. Pouget, M. Raux, T. Similowski, M. Chavez

Research output: Contribution to journalArticlepeer-review

21 Citations (Scopus)


Goal: During mechanical ventilation, patient-ventilator disharmony is frequently observed and may result in increased breathing effort, compromising the patient's comfort and recovery. This circumstance requires clinical intervention and becomes challenging when verbal communication is difficult. In this study, we propose a brain-computer interface (BCI) to automatically and noninvasively detect patient-ventilator disharmony from electroencephalographic (EEG) signals: a brain-ventilator interface (BVI). Methods: Our framework exploits the cortical activation provoked by the inspiratory compensation when the subject and the ventilator are desynchronized. Use of a one-class approach and Riemannian geometry of EEG covariance matrices allows effective classification of respiratory states. The BVI is validated on nine healthy subjects that performed different respiratory tasks that mimic a patient-ventilator disharmony. Results: Classification performances, in terms of areas under receiver operating characteristic curves, are significantly improved using EEG signals compared to detection based on air flow. Reduction in the number of electrodes that can achieve discrimination can be often desirable (e.g., for portable BCI systems). By using an iterative channel selection technique, the common highest order ranking, we find that a reduced set of electrodes (=6) can slightly improve for an intrasubject configuration, and it still provides fairly good performances for a general intersubject setting. Conclusion: Results support the discriminant capacity of our approach to identify anomalous respiratory states, by learning from a training set containing only normal respiratory epochs. Significance: The proposed framework opens the door to BVIs for monitoring patient's breathing comfort and adapting ventilator parameters to patient respiratory needs.

Original languageEnglish
Article number7516574
Pages (from-to)1138-1148
Number of pages11
JournalIEEE Transactions On Biomedical Engineering
Issue number5
Early online date19 Jul 2016
Publication statusPublished - May 2017
Externally publishedYes


  • biomedical monitoring
  • Biomedical signal processing
  • brain-computer interfaces (BCI)
  • electroencephalography (EEG)
  • medical signal detection


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