EEG Source Imaging Enhances the Decoding of Complex Right-Hand Motor Imagery Tasks.
OBJECTIVE
Sensorimotor-based brain-computer interfaces (BCIs) have achieved successful control of real and virtual devices in up to three dimensions; however, the traditional sensor-based paradigm limits the intuitive use of these systems. Many control signals for state-of-the-art BCIs involve imagining the movement of body parts that have little to do with the output command, revealing a cognitive disconnection between the user's intent and the action of the end effector. Therefore, there is a need to develop techniques that can identify with high spatial resolution the self-modulated neural activity reflective of the actions of a helpful output device.


METHODS
We extend previous EEG source imaging (ESI) work to decoding natural hand/wrist manipulations by applying a novel technique to classifying four complex motor imaginations of the right hand: flexion, extension, supination, and pronation.


RESULTS
We report an increase of up to 18.6% for individual task classification and 12.7% for overall classification using the proposed ESI approach over the traditional sensor-based method.


CONCLUSIONS
ESI is able to enhance BCI performance of decoding complex right-hand motor imagery tasks.


CONCLUSIONS
This study may lead to the development of BCI systems with naturalistic and intuitive motor imaginations, thus facilitating broad use of noninvasive BCIs.
Bradley J
BJ
Edelman
Bryan
B
Baxter
Bin
B
He
D000293:Adolescent; D000328:Adult; D001931:Brain Mapping / Q000379:methods*; D062207:Brain-Computer Interfaces; D004569:Electroencephalography / Q000379:methods*; D005260:Female; D006225:Hand / Q000502:physiology*; D006801:Humans; D007092:Imagination / Q000502:physiology*; D008297:Male; D055815:Young Adult