Brain computer interfaces (BCIs) offer individuals suffering from majordisabilities an alternative method to interact with their environment.Sensorimotor rhythm (SMRs) based BCIs can successfully perform control tasks;however, the traditional SMR paradigms intuitively disconnect the control andreal task, making them non-ideal for complex control scenarios. In this study,we design a new, intuitively connected motor imagery (MI) paradigm usinghierarchical common spatial patterns (HCSP) and context information toeffectively predict intended hand grasps from electroencephalogram (EEG) data.Experiments with 5 participants yielded an aggregate classificationaccuracy--intended grasp prediction probability--of 64.5\% for 8 different handgestures, more than 5 times the chance level.
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