Article

For brain computer interfaces (BCIs), which may be valuable in
neurorehabilitation, brain signals derived from mental activation can be
monitored by non-invasive methods, such as functional near-infrared spectroscopy
(fNIRS). Single-trial classification is important for this purpose and this was
the aim of the presented study. In particular, we aimed to investigate a combined
approach: 1) offline single-trial classification of brain signals derived from a
novel wireless fNIRS instrument; 2) to use motor imagery (MI) as mental task
thereby discriminating between MI signals in response to different tasks
complexities, i.e. simple and complex MI tasks. METHODS: 12 subjects were asked
to imagine either a simple finger-tapping task using their right thumb or a
complex sequential finger-tapping task using all fingers of their right hand.
fNIRS was recorded over secondary motor areas of the contralateral hemisphere.
Using Fisher's linear discriminant analysis (FLDA) and cross validation, we
selected for each subject a best-performing feature combination consisting of 1)
one out of three channel, 2) an analysis time interval ranging from 5-15 s after
stimulation onset and 3) up to four Delta[O2Hb] signal features (Delta[O2Hb] mean
signal amplitudes, variance, skewness and kurtosis). RESULTS: The results of our
single-trial classification showed that using the simple combination set of
channels, time intervals and up to four Delta[O2Hb] signal features comprising
Delta[O2Hb] mean signal amplitudes, variance, skewness and kurtosis, it was
possible to discriminate single-trials of MI tasks differing in complexity, i.e.
simple versus complex tasks (inter-task paired t-test p </= 0.001), over
secondary motor areas with an average classification accuracy of 81%.
CONCLUSIONS: Although the classification accuracies look promising they are
nevertheless subject of considerable subject-to-subject variability. In the
discussion we address each of these aspects, their limitations for future
approaches in single-trial classification and their relevance for
neurorehabilitation.

Langue : ANGLAIS