Authors:
Marc Tabie
1
;
Hendrik Woehrle
2
and
Elsa Andrea Kirchner
3
Affiliations:
1
University of Bremen, Germany
;
2
German Research Center for Artificial Intelligence (DFKI), Germany
;
3
University of Bremen and German Research Center for Artificial Intelligence (DFKI), Germany
Keyword(s):
Movement Prediction, EEG, EMG, Online Classifier Calibration.
Related
Ontology
Subjects/Areas/Topics:
Animation and Simulation
;
Biomedical Engineering
;
Biomedical Signal Processing
;
Computer Vision, Visualization and Computer Graphics
;
Detection and Identification
;
Motion Control
;
Real-Time Systems
Abstract:
Prediction of voluntary movements from electroencephalographic (EEG) signals is widely used and investigated
for applications like brain-computer interfaces (BCIs) or in the field of rehabilitation. Different combinations
of signal processing and machine learning methods can be found in literature for solving this task.
Machine learning algorithms suffer from small signal-to-noise ratios and non-stationarity of EEG signals. Due
to the non-stationarity, prediction performance of a fixed classifier may degrade over time. This is because
the shape of motor-related cortical potentials associated with movement prediction change over time and thus
may no longer be well represented by the classifier. A solution is online calibration of the classifier. Therefore,
we propose a novel approach in which movement onsets, detected by the analysis of electromyographic
(EMG) signals are used to recalibrate the classifier during runtime. We conducted experiments with 8 subjects
performing self-initia
ted, self-paced movements of the right arm. We investigated the differences of online
calibration versus applying a fixed classifier. Further the effect of varying initial training instances ( 1
3 or 2
3 of
available data) was examined. In both cases we found a significant improvement in prediction performance
(p < 0:05) when the online calibration was used.
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