A comparison of non-linear methods for the decoding of motor performance.
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2021-06-22
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en
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Abstract
Currently, work is underway to create a closed-loop deep brain stimulation
(DBS) system. This research attempts to contribute to the creation of a
closed-loop DBS system by improving motor labels used in the search for
informative brain signals. This was done by building on earlier research
that used a motor task called the copy-draw task, to evaluate ne-grained
hand motor performance under DBS-on and DBS-o conditions. The earlier
research used linear discriminant analysis (LDA) to reduce the large feature
dimensionality to a single scalar value representing the motor score, which
could then be used in conjunction with source power comodulation to nd
informative brain signals. However, LDA makes the assumption that the
underlying features distributions are Gaussian. We showed that this assumption
is not met by the data, and we therefore tested whether logistic
regression, random forest, and the support vector classi er, which do not
make the assumption of Gaussian features yield better motor decoding performance.
We evaluated the new methods using a nested cross-validation
procedure with hyperparameter optimization. We also tested whether removing
linear trends from the data, or splitting trials of the copy-draw task
to create more training data yielded better motor decoding performance.
We demonstrate that there is no signi cant increase in motor decoding performance
by logistic regression, random forest, or support vector classi er.
Moreover, we saw no increase in the motor decoding performance by using
the data where linear trends were removed, or where the trials were split to
create more training data. Our research therefore shows that in this problem
setting LDA is robust to non-Gaussian features.
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Faculteit der Sociale Wetenschappen