Publikation
Learning from the past: post processing of classification scores to find a more accurate and earlier movement prediction
Sirko Straube; David Feess; Anett Seeland
In: Jose Luis Pons; Rovira Pedro Encarnacao; Ana Rita Londral (Hrsg.). Neurotechnology, Electronics, and Informatics: Revised Selected Papers from Neurotechnix 2013. Pages 91-107, Springer Series in Computational Neuroscience, Vol. 13, ISBN 978-3-319-15997-3, Springer International Publishing, 2015.
Zusammenfassung
Brain-computer interfaces performing movement prediction
are useful in a variety of application fields from telemanipulation to
rehabilitation. However, current systems still struggle with a level of
unreliability requiring improvement, so that the full potential of these
systems can be used in the future. Here, we suggest to improve the performance
and robustness of classification outcomes by postprocessing the
raw score values with the history of previous classifications. For this several
postprocessing methods that operate on the classification outcomes
are investigated. In particular, the data was classified after preprocessing
using a support vector machine (SVM). The output of the SVM,
i.e. the raw score values, were postprocessed using previously obtained
scores to account for trends in the classification result. The respective
methods difier in the way the transformation is performed. The idea is
to use trends, like the rise of the score values approaching an upcoming
movement, to yield a better prediction in terms of detection accuracy
and/or an earlier time point. We present results from different subjects
where upcoming voluntary movements of the right arm were predicted
using movement related cortical potentials from the EEG. The results
illustrate that better and earlier predictions are indeed possible with the
suggested methods. However, the best postprocessing method was rather
subject-specific. Finally, we use straightforward ensemble approaches to
exemplify how the methods can be directly used in an application and
how this can in
uence the overall movement prediction performance. Depending
on the requirements of the application at hand, postprocessing
the classification scores as suggested here can be used to find the best
compromise between prediction accuracy and time point.