**3. Methods**

*Assistive and Rehabilitation Engineering*

loop. Current technology, however, is not able to address the complex issue of hand function, which involves overlapping neuro-physio strategies and multiple degrees of freedom. At most, simple movements may be possible [32] which has been shown to not adequately impact function for the highly heterogeneous stroke affected population. Gross movements can be expected to improve with very high number of repetitions, thus enabling the brain to rewire itself in a limited way. However, there is poor evidence that such gross movement practice translates significantly into function. Therefore, the modification to the above model is proposed, incorporating the feedforward and feedback elements modeled in **Figure 2** as a form of

The augmented feedback may be delivered visually via a muscle-brain-computer interface. The feedforward in the form of appropriate audio-visual inputs, which lead the human to attempt a series of desired actions through imitation, is known to facilitate recovery [33]. Moreover, there is evidence of perception transferring to action and more importantly, from action to perception [34]. The augmented feedback is expected to drive motor intention and exploration while the feedforward is expected to prime the brain for motor actuation and goal directed learning through imitation. From a functional improvement perspective, the augmented feedback

augmentation to help overcome the deficits through the learning route.

may be customized for a person using time-locked parameters as follows:

The brain and the body are inseparably linked, and both contribute significantly for neuroplasticity to occur and health parameters to improve [35]. Based on this understanding of how human learning may be applied practically in the context of post-stroke rehabilitation, this study was conceived with the following

1.When EEG and EMG signals during activity are brought together in a timesynchronized manner for real-time feedback along with an audio-visual feedforward for imitation, it provides an opportunity for the patient to work with sensory, exploratory and goal-directed learning toward functional

2.Displaying quantified, relative brain and muscle feedback in real-time while training activation and relaxation simultaneously during movement or while attempting to manipulate objects, will enhance the conditions for incremental associative learning of overlapping brain and muscle strategies to occur [36]. Under such conditions, subjects may potentially achieve systemic gains in functional performance, even though they may have tried existing rehabilita-

This paper describes a bio-mechatronics approach to understanding where re-learning is misled or failing and uses a "feedforward-feedback" modality to help chronic stroke subjects train gross movements (as measured by Fugyl Meyer Upper Extremity Motor Assessment scale) and functional, timed-task capabilities (as measured by Action Research Arm Test). The SynPhNe system employs learning and training principles similar to that which babies seem to use in the design of its user interface, to leverage the mechanism of "self-regulation" or "self-correction." The study explores to what extent such real-time "self-correction" alone, in the absence of any form of external stimulation or robotic assistance, impacts the

1.EMG agonist-antagonist balance (muscle strategy).

2.EEG relaxation and attention states (brain strategy).

tion methods and only partially succeeded.

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assumptions:

rehabilitation goals.
