Riemannian geometry for code-modulated visual evoked potential brain-computer interfaces: Towards calibration-less brain-computer interface
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2021-06-18
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en
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Abstract
A brain-computer interface (BCI) is a system that makes it possible to have
a direct communication between the brain and a computer. To make a step
towards practical BCI systems we want to improve the decoding and shorten
calibration time. A method that has been gaining more popularity in the
BCI community is the Riemannian geometry framework. To our knowledge,
so far no code-modulated visual evoked potential (cVEP) based BCI has
adapted a Riemannian approach. Therefore, it is yet unknown if the Riemannian
framework could possibly improve or have the same performance
as the current state of the art cVEP based methods. To answer the question
if this Riemannian framework could be used for cVEP BCIs I compare
the performance of the minimum distance to the Riemannian mean (MDM)
classi er to a baseline method, which is linear discriminant analysis (LDA).
Two dimensionality methods are introduced namely, canonical correlation
analysis (CCA) and cherry picking. Multiple scenarios are tested these include
a small and larger training dataset, the use of one or two (virtual)
channels and for the MDM an extra scenario where the use of both prototypes
or only the target prototype is investigated. The MDM classi er shows
comparable overall results with LDA. Additionally, the use of two (virtual)
channels signi cantly outperforms (p < 0:05) the use of one (virtual) channel.
Lastly, the use of both prototypes is not signi cantly different from
only using the target prototype. However, it could improve computational
effciency to only use the target prototype. Overall I can say that the use
of MDM and in turn the Riemannian framework can be recommended for
cVEP based BCIs.
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Faculteit der Sociale Wetenschappen