| Summary: | A new approach in motor rehabilitation after stroke is to use motor imagery (MI). To give feedback on MI performance BCIs can be used. This requires a fast and easy acquisition of a reliable classifier. Usually, for training a classifier, EEG data of motor imagery without feedback is used, but it would be advantageous if we could give feedback right from the beginning. The sensorimotor EEG changes of the motor cortex during active and passive movement and motor imagery are similar. The aim of this study is to explore, whether it is possible to use EEG data from active or passive movement to set up a classifier for the detection of motor imagery in a group of elderly persons. In addition, the activation patterns of the motor cortical areas of elderly persons were analysed during different motor tasks. EEG was recorded from three Laplacian channels over the sensorimotor cortex in a sample of 19 healthy elderly volunteers. Participants performed three different tasks in consecutive order, passive, active hand movement and hand motor imagery. Classifiers were calculated with data of every task. These classifiers were then used to detect ERD in the motor imagery data. ERD values, related to the different tasks, were calculated and analysed statistically.The performance of classifiers calculated from passive and active hand movement data did not differ significantly regarding the classification accuracy for detecting motor imagery. The EEG patterns of the motor cortical areas during the different tasks was similar to the patterns normally found in younger persons but more widespread regarding localization and frequency range of the ERD.In this study, we have shown that it is possible to use classifiers calculated with data from passive and active hand movement to detect motor imagery. Hence, for working with stroke patients, a physiotherapy session could be used to obtain data for classifier setup and the BCI rehabilitation training could start immediately.
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