3 research outputs found
Decoding Complex Imagery Hand Gestures
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