Abstract
Non-invasive Brain-Computer-Interfaces (BCIs) are devices that infer the intention of human subjects from
signals generated by the central nervous system and
recorded outside the skull, e.g., by electroencephalography
(EEG). They can be used to enable basic communication
for patients who are not able to communicate by
normal means, e.g., due to neuro-degenerative diseases
such as amyotrophic lateral sclerosis (ALS) (see
[Vaughan2003] for a review).
One challenge in research on BCIs is minimizing the
training time prior to usage of the BCI. Since EEG
patterns vary across subjects, it is usually necessary to
record a number of trials in which the intention of the
user is known to train a classifier. This classifier is
subsequently used to infer the intention of the BCI-user.
In this paper, we present the application of an
unsupervised classification method to a binary noninvasive
BCI based on motor imagery. The result is a
BCI that does not require any training, since the
mapping from EEG pattern changes to the intention of
the user is learned online by the BCI without any
feedback. We present experimental results from six
healthy subjects, three of which display classification
errors below 15. We conclude that unsupervised BCIs
are a viable option, but not yet as reliable as supervised
BCIs.
The rest of this paper is organized as follows. In the
Methods section, we first introduce the experimental
paradigm. This is followed by a description of the
methods used for spatial filtering, feature extraction,
and unsupervised classification. We then present the
experimental results, and conclude the paper with a
brief discussion.
