Decoding mental representations for neurofeedback in motor and imagery tasks

The inability to execute coordinated movements can have a profound impact on an individual’s ability to complete the activities of daily living. Often times, these impairments arise as a result of neurological injury such as a stroke. Conventional physical therapy of the affected limb is often ineffective, possibly because it treats the symptom rather than the cause of the problem: impaired motor control circuitry in the brain. A potential method to address this issue is to employ neuroimaging technology to guide neuroplastic changes in the brain to restore motor function. The first aim of this dissertation was to develop and validate the use of a neurofeedback training target derived from functionally aligned data of healthy individuals. Our results showed a significant improvement in classification accuracy of individual finger presses when group data was aligned based on function rather than anatomy. This indicates that our functionally aligned template could provide an effective target for neural reinforcement of finger individuation beyond traditional anatomically aligned templates. For more severely impaired individuals, neuroimaging can also be used to develop assistive technologies by creating a connection between the brain and an external device, a technology known as a Brain-Computer Interface (BCI). In this work, we sought to address some of the current gaps in BCI usability by investigating the implementation of a visual imagery control paradigm. The second aim in this dissertation attempted to characterize the flexibility of visual imagery by mapping out the neural representational space of imagery for multiple image categories. While we were able to significantly identify when an individual was engaged in visual imagery compared to resting, our classifier was unable to accurately discern between the category-level neural activity. Our third aim expounded on this topic by investigating some of the key experimental components that contribute to successful performance. The results showed that visual imagery following a perception cue provided a stronger, more easily classifiable signal than visual imagery following an auditory cue. These results indicate that visual imagery is a challenging, yet possible BCI paradigm that could be useful in situations where other methods are ill suited.