Brain Sci. 2013, 3(4), 1554-1568; doi:10.3390/brainsci3041554
Opinion

Patient Machine Interface for the Control of Mechanical Ventilation Devices

1,2,* email, 1,3email and 2email
Received: 19 August 2013; in revised form: 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)
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Abstract: 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.
Keywords: patient machine interface; mechanical ventilation; neurodrive; EEG (electroencephalography); breathing; BCI (brain computer interfaces)
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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.

AMA Style

Grave de Peralta R, Gonzalez Andino S, Perrig S. Patient Machine Interface for the Control of Mechanical Ventilation Devices. Brain Sciences. 2013; 3(4):1554-1568.

Chicago/Turabian Style

Grave de Peralta, Rolando; Gonzalez Andino, Sara; Perrig, Stephen. 2013. "Patient Machine Interface for the Control of Mechanical Ventilation Devices." Brain Sci. 3, no. 4: 1554-1568.

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