Article

A well-known neurophysiological marker that can easily be captured with
electroencephalography (EEG) is the so-called P300: a positive signal deflection
occurring at about 300 ms after a relevant stimulus.
This brain response is
particularly salient when the target stimulus is rare among a series of
distracting stimuli, whatever the type of sensory input. Therefore, it has been
proposed and extensively studied as a possible feature for direct brain-computer
communication. The most advanced non-invasive BCI application based on this
principle is the P300-speller. However, it is still a matter of debate whether
this application will prove relevant to any population of patients. In a series
of recent theoretical and empirical studies, we have been using this P300-based
paradigm to push forward the performance of non-invasive BCI. This paper
summarizes the proposed improvements and obtained results. Importantly, those
could be generalized to many kinds of BCI, beyond this particular application.
Indeed, they relate to most of the key components of a closed-loop BCI, namely:
improving the accuracy of the system by trying to detect and correct for errors
automatically; optimizing the computer's speed-accuracy trade-off by endowing it
with adaptive behavior; but also simplifying the hardware and time for set-up in
the aim of routine use in patients. Our results emphasize the importance of the
closed-loop interaction and of the ensuing co-adaptation between the user and the
machine whenever possible. Most of our evaluations have been conducted in healthy
subjects. We conclude with perspectives for clinical applications.
CI - Copyright (c) 2015 Elsevier Masson SAS. All rights reserved.

Langue : ANGLAIS