Toward Mutually Adaptive Brain Computer Interfacing
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2009-05-19
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en
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Abstract
Brain-computer interfaces (BCIs) measure brain activity and convert it to
commands that can be executed by a device. A good BCI enables users to
control a device by means of thought. In this thesis we consider two types
of brain-computer interfacing: static BCI and mutually adaptive BCI.
Research on static BCI generally proceeds as follows: 1. Data are collected;
2. a mapping is learned from these data that converts brain activity
to commands; 3. the learned mapping is evaluated on test data. Care must
be taken to fairly compare different methods that learn such a mapping.
First, the quality of a method can only be estimated fairly if all decisions
are independent of the test data. Second, to test for significant differences
between methods, the variance of each method’s performance over data sets
needs to be considered. We discuss why it is difficult to compare methods
using a sample validation or a k-fold cross-validation design. Furthermore,
we discuss the 5 × 2 cross-validation design proposed by [12], which controls
for variation in performance caused by the selection of training and
test data.
The BCI group of the Nijmegen Institute for Cognition and Information
(NICI) [18] collected data using an imagined time-locked hand tapping design.
To these data we applied a number of different methods that deal with
channel and time information and we compared their performance. The results
were not satisfactory. Besides low performance overall, methods that
we expected to perform relatively well, performed relatively poor, and vice
versa. Most notably, using a selection of channels never performed better
than using all channels. Furthermore, most differences between methods
were not significant. We conclude that the data are of low quality and that
we should not use it to choose a method for further usage.
We argue that static BCI does not suffice for real-world applications.
In order to make possible efficient communication between a user and a device,
a BCI needs to adapt. First, adaptation is needed to avoid decreases in
performance due to unintended changes in brain activity [60]. Second, adaptation is needed to profit from the adaptive capacities of the human brain,
thereby improving communication over time. Interestingly, at this moment
there are few BCIs that combine continuous adaptation with feedback based
on their resulting dynamic state. We believe that this combination is essential
to make BCI successful.
It is difficult to model the dynamics of a user’s brain explicitly. However,
models can be build that react to adaptation on behalf of the user by
assigning higher relevance to data that have been measured more recently.
We introduce a new method which realizes this for regressive Gaussian processes:
optimized decrease of relevance (ODR). ODR uses a kernel that
takes into account each data point’s time of measurement. This temporal
kernel induces a graded window over the training data. The steepness of
this graded window corresponds to the speed by which the relevance of data
points for making new predictions decreases over time. This steepness can
be learned automatically from the data. Furthermore, by placing a regular
sliding window on top of the graded window, the relevance of time itself can
be made dynamic. As a proof of concept, we apply ODR to artificial data
and show its advantages.
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Faculteit der Sociale Wetenschappen