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

• Published in: IEEE transactions on biomedical engineering Vol. 64; no. 5; pp. 1138 - 1148
• Main Authors: Navarro-Sune, X., Hudson, A. L., Fallani, F. De Vico, Martinerie, J., Witon, A., Pouget, P., Raux, M., Similowski, T., Chavez, M.
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.05.2017; The Institute of Electrical and Electronics Engineers, Inc. (IEEE); Institute of Electrical and Electronics Engineers
• Subjects: Adult; Air flow; biomedical monitoring; Biomedical signal processing; Brain; Brain - physiology; Brain-Computer Interfaces; brain–computer interfaces (BCI); Breathing; Classification; Computer applications; Computer Science; Cortex; Covariance matrices; Covariance matrix; Diagnosis, Computer-Assisted - methods; EEG; Electrodes; Electroencephalography; electroencephalography (EEG); Electroencephalography - methods; Feature extraction; Female; Geometry; Human-Computer Interaction; Human-computer interface; Humans; Implants; Iterative methods; Machine Learning; Male; Mechanical ventilation; medical signal detection; Pattern Recognition, Automated - methods; Prototypes; Respiration; Respiration, Artificial - methods; Respiratory Mechanics - physiology; Synchronization; Ventilation; Ventilators; Verbal communication

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 difficul...