Enhancing the Representational Similarity Between Execution and Imagination of Movement Using Network-Based Brain Computer Interfacing

Motor imagery-based brain computer interfacing (MI-BCI) as a neuro-rehabilitation tool aims at facilitating motor improvement using mental practice. However, the effectiveness of MI-BCI in producing clinically meaningful functional outcome is debated. Aside from computational shortcomings, a main limiting obstacle seems to be the substantial representational dissimilarity between movement imagination (MI) and movement execution (ME) on the level of engaged neural networks. This dissimilarity renders inducing functionally effective and long lasting changes in motor behavior through MI challenging. Moreover, the quality and intensity of imagination is highly prone to change on a trial-to-trial basis, based on the subject's state of mind and mental fatigue. This leads to an inconsistent profile of neural activity throughout training, limiting learning in a Hebbian sense. To address these issues, we propose a neuroconnectivity-based paradigm, as a systematic priming technique to be utilized pre-BCI training. In the proposed paradigm, ME-idle representational dissimilarity network (RDN) features are used to detect MI in real-time. This means that to drive the virtual environment, an ME-like activation pattern has to be learned and generated in the brain through MI. This contrasts with conventional BCIs which consider a successful MI, one that results in higher than a threshold change in the power of sensorimotor rhythms. Our results show that four out of five participants achieved a consistent session-to-session enhancement in their net MI-ME network-level similarity (mean change rate of 6.16% {+/-} 4.64 per session). We suggest that the proposed paradigm, if utilized as a priming technique pre-BCI training, can potentially enhance the neural and functional effectiveness. This can be achieved through 1- shifting MI towards engaging ME-related networks to a higher extent, and 2- inducing consistency in MI quality by using the ME-related networks as the ground-truth and thus enhancing the robustness of the activity pattern in the brain. This would in turn lend to the clinical acceptability of BCI as a neurorehabilitation tool.