Brain computer interfaces (BCIs) offer individuals suffering from major
disabilities 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 and
real task, making them non-ideal for complex control scenarios. In this study,
we design a new, intuitively connected motor imagery (MI) paradigm using
hierarchical common spatial patterns (HCSP) and context information to
effectively predict intended hand grasps from electroencephalogram (EEG) data.
Experiments with 5 participants yielded an aggregate classification
accuracy--intended grasp prediction probability--of 64.5\% for 8 different hand
gestures, more than 5 times the chance level.Comment: This work has been submitted to EMBC 201