Decoding Complex Imagery Hand Gestures
This addresses the need for more intuitive control in BCIs for individuals with disabilities, though it appears incremental as it builds on existing motor imagery paradigms.
The study tackled the problem of predicting intended hand grasps from EEG data for brain-computer interfaces, achieving an aggregate classification accuracy of 64.5% for 8 gestures, which is over 5 times the chance level.
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.