Abstract
Current EEG based brain computer interface (BCI) systems have achieved successful control in up to 3 dimensions; however, the current sensor-based paradigm is not well suited for many rehabilitative and recreational applications that require motor imagination (MI) tasks of fine motor movements to be recognized. Therefore there is a great need to find complex MI tasks that are intuitive for BCI users to perform and that can be classified with high accuracy. In this paper we present our results on classifying four MI tasks of the right hand, flexion, extension, supination and pronation using a novel EEG source imaging approach. Using this approach we were able to improve the four-class classification of the four tasks by nearly 10% as compared to traditional sensor-based techniques.
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