Bradley J. Edelman, Bryan Baxter, Bin He, University of Minnesota, USA
Brain-computer interfaces (BCIs) based on sensorimotor rhythms (SMRs) have achieved successful control of real and virtual devices in up to three dimensions. SMR BCI control signals are founded on the user’s ability to modulate frequency-specific neural activity in the primary motor cortex through the imagination of motor tasks. However, many control signals for state-of-the-art SMR 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 motor intent and the action of the end effector. Therefore, there is a need to develop techniques which can identify with high spatial resolution the self-modulated neural activity reflective of the actions of a helpful output device. Over the past decade EEG source imaging (ESI) techniques have proven to be an effective approach for interpreting motor intent by reconstructing the current density on the cortical surface. We extend previous ESI work to natural hand manipulations by applying a novel technique to classifying four complex motor imaginations of the right hand: flexion, extension, supination, and pronation. We report an increase of between 6.6% and 18.6% for individual task classification, and an increase of 12.7% for the overall classification using the proposed ESI approach over the traditional sensor-based method. Additionally, we observed overlapping and yet distinct cortical representations contributing to the separation of the four right-hand tasks that support a functional somatotopic representation of the primary motor cortex. The successful separation of these complex tasks in an offline setting provides confidence for developing an SMR BCI for the natural control of external devices using realistic motor imaginations. For more information, please visit our group website at helab.umn.edu.
Keywords: Brain-computer interface, Brain mapping, EEG source imaging, Motor imagery, Neuroimaging