Publikation
Online Classifier Adaptation for the Detection of P300 Target Recognition Processes in a Complex Teleoperation Scenario
Hendrik Wöhrle; Elsa Andrea Kirchner
In: H.P. da Silva; A. Holzinger; S. Fairclough; D. Majoe. Physiological Computing Systems. Pages 105-119, ISBN 978-3-662-45685-9, Springer, 2014.
Zusammenfassung
The detection of event related potentials and their usage for innovative applications became an increasingly important research topic for brain computer interfaces in the last couple of years. However, brain computer interfaces use methods that need to be trained on subjectspecific data before they can be used. This problem must be solved for real-world applications in which humans are multi tasking and hence are to some degree are less predictable in their behavior compared to classical set ups for brain computer interfacing. In this paper, we show the detection and passive usage of the P300 related brain activity in a highly uncontrolled and noisy application scenario. The subjects are multi tasking, i.e., they perform a demanding senso-motor task, i.e., the telemanipulate a real robotic arm while responding to important messages. For telemanipulation, the subject wears an active exoskeleton to control a robotic arm, which is presented to him in a virtual scenario. By online analysis of the subject's electroencephalogram we detect P300 related target recognition processes to infer on upcoming response behavior on presented task-relevant messages (Targets) or missing of response behavior in case a Target was not recognized. We show that a classifier that is trained to distinguish between brain activity evoked by recognized task-relevant stimuli (recognized Targets) and ignored frequent task-irrelevant stimuli (Standards) can be applied to classify between brain activity evoked by recognized targtes and brain activity that is evoked in case that task-relevant stimuli are not recognized (Missed Targets). The applied transfer of classifier results in reduced performance. We show that this draw back of the approach can strongly be improved by using online machine learning tools to adapt the pre-trained classifier to the new class, i.e., to the Missed Target class, that was not used during training of the classifier.