Next Article in Journal / Special Issue
A Brain-Computer-Interface for the Detection and Modulation of Gamma Band Activity
Previous Article in Journal
Olfactory Hallucinations without Clinical Motor Activity: A Comparison of Unirhinal with Birhinal Phantosmia
Article Menu

Export Article

Open AccessOpinion
Brain Sci. 2013, 3(4), 1554-1568;

Patient Machine Interface for the Control of Mechanical Ventilation Devices

Electrical Neuroimaging Group, Albert Gos 18, Geneva 1206, Switzerland
Geneva Sleep Lab, Department of Neuropsychiatry, Geneva University Hospital (HUG), Geneva 1225, Switzerland
Neural Microcircuitry Lab, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne 1015, Switzerland
Author to whom correspondence should be addressed.
Received: 19 August 2013 / Revised: 12 September 2013 / Accepted: 8 November 2013 / Published: 15 November 2013
(This article belongs to the Special Issue Emergence of Novel Brain-Computer Interface Applications)
Full-Text   |   PDF [432 KB, uploaded 15 November 2013]   |  


The potential of Brain Computer Interfaces (BCIs) to translate brain activity into commands to control external devices during mechanical ventilation (MV) remains largely unexplored. This is surprising since the amount of patients that might benefit from such assistance is considerably larger than the number of patients requiring BCI for motor control. Given the transient nature of MV (i.e., used mainly over night or during acute clinical conditions), precluding the use of invasive methods, and inspired by current research on BCIs, we argue that scalp recorded EEG (electroencephalography) signals can provide a non-invasive direct communication pathway between the brain and the ventilator. In this paper we propose a Patient Ventilator Interface (PVI) to control a ventilator during variable conscious states (i.e., wake, sleep, etc.). After a brief introduction on the neural control of breathing and the clinical conditions requiring the use of MV we discuss the conventional techniques used during MV. The schema of the PVI is presented followed by a description of the neural signals that can be used for the on-line control. To illustrate the full approach, we present data from a healthy subject, where the inspiration and expiration periods during voluntary breathing were discriminated with a 92% accuracy (10-fold cross-validation) from the scalp EEG data. The paper ends with a discussion on the advantages and obstacles that can be forecasted in this novel application of the concept of BCI. View Full-Text
Keywords: patient machine interface; mechanical ventilation; neurodrive; EEG (electroencephalography); breathing; BCI (brain computer interfaces) patient machine interface; mechanical ventilation; neurodrive; EEG (electroencephalography); breathing; BCI (brain computer interfaces)

Figure 1

This is an open access article distributed under the Creative Commons Attribution License (CC BY 3.0).

Share & Cite This Article

MDPI and ACS Style

Grave de Peralta, R.; Gonzalez Andino, S.; Perrig, S. Patient Machine Interface for the Control of Mechanical Ventilation Devices. Brain Sci. 2013, 3, 1554-1568.

Show more citation formats Show less citations formats

Related Articles

Article Metrics

Article Access Statistics



[Return to top]
Brain Sci. EISSN 2076-3425 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top