Abstract
Stroke is the fifth leading cause of death and disability in the United States with approximately 6.8 million people living with residual deficits and approximately $34 billion spent on treatment annually [1, 2]. Simultaneously, dramatic healthcare shifts have limited extended care accessibility, with many individuals discharged from restorative therapy by three-months post-stroke. Decreased access and increased costs have led clinicians, and scientists to investigate more effective and efficient interventions to improve the function of the hemiparetic upper extremity of individuals post-stroke. One such modality is a brain-computer interface (BCI) technology that utilizes brain signals to drive rehabilitation of motor function. Emerging data suggests the use of BCI for motor rehabilitation post-stroke, facilitating an individual’s return to function and improving quality of life [3,4,5,6,7,8,9,10]. Specifically, integration of virtual reality (VR) and functional electrical stimulation (FES) components is an innovative rehabilitation strategy with a strong potential to reinstitute central motor programs specific to hand function in patients’ status post-stroke. By utilizing the Fugl-Meyer Assessment (FMA), researchers can monitor the motor function of the hemiparetic upper extremity pre/post-intervention, objectively quantifying the effectiveness of BCI for the restoration of upper extremity motor function [11]. Neurophysiological brain imaging techniques allow tracking changes in the neural substrates of motor function due to BCI intervention. Therefore, the purpose of our study is to demonstrate the utility of BCI-VR-FES intervention for motor rehabilitation of upper extremity, based upon the theory of neuroplasticity, in individuals’ post-stroke by using functional (FMA) and neurophysiological outcome measures.
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Acknowledgments
We would like to thank both the g.tech company, Austria for providing us with the RecoveriX device, as well as AdventHealth Sports Medicine & Rehabilitation for their collaboration on this study.
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Appendices
Appendix 1
Anticipated Implementation: BCI-VR-FES | ||
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1. Use it or lose it | Failure to drive specific brain functions can lead to functional degradatio | Can be used on any stages after the traumatic brain event to facilitate motor learning and neuroplasticity |
2. Use it and improve It | Training that drives a specific brain function can lead to an enhancement of that function | BCI-VR-FES utilizes a brain signal recorded from sensory-motor cortex to facilitate movement and provide sensory-motor-visual feedback |
3. Specificity | The nature of the training experience dictates the nature of the plasticity | When training with BCI-VR-FES system, the patient is asked to visualize (imagine or even attempt) a skilled movement that he/she was able to perform before the stroke (e.g., hitting a tennis ball, playing a guitar, combing hair) |
4. Repetition matters | Induction of plasticity requires sufficient repetition | BCI-VR-FES system provides hundreds of repetitions within relatively short period of time |
5. Intensity matters | Induction of plasticity requires sufficient training intensity | The intensity of BCI-VR-FES training can be adjusted dependent on patient’s capability and can vary within a broad range of intensity to promote neurorecovery |
6. Time matters | Different forms of plasticity occur at different times during training | BCI-VR-FES approach has demonstrated its usefulness both for chronic and acute stroke patients. Moreover, it can be used on all stages of stroke recovery |
7. Salience matters | The training experience must be sufficiently salient to induce plasticity | BCI-VR-FES system provides constant feedback each time the patient is activating his/her sensory-motor cortex during motivational tasks, thus ensuring the salience of patient’s experience during sensory-motor cortex activation versus other non-related activation |
8. Age matters | Training-induced plasticity occurs more readily in younger brains | Although the plastic brain changes may be less profound in aging brain than in younger brain, the combination of approaches available within BCI-VR-FES allows for successful reorganization of brain tissue to produce desired outcomes |
9. Transference | Plasticity in response to one training experience can enhance the acquisition of similar behaviors | It has been demonstrated that the BCI-VR-FES effect in restoring the function of upper extremity influences restoring the overall posture and the function of lower extremities through the promotion of concurrent/subsequent plasticity |
10. Interference | Plasticity in response to one experience can interfere with the acquisition of other behaviors | BCI-VR-FES will occur outside of a subject’s other therapies, potentiating neurorecovery to augment other therapeutic protocols, and not interfere with plasticity |
Appendix 2
Publication | Type of stroke | Age of subjects (years) | Time since stroke (months) | Stratification |
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Woytowicz [30] | n/a | 58.6 ± 11.8 | >6 | With reflexes: Severe (0–15), Severe-Moderate (16–34), Moderate-Mild (35–53), Mild (54–66); w/o reflexes: Severe (0–12), Severe-Moderate (13–30), Moderate-Mild (31–47), Mild (48–60) |
Hoonhorst [31] | ischemic | 64.8 ± 12.5 | 6 | With reflexes: No UL motor capacity (0–22), Poor (23–31), Limited (32–47), Notable (48–52), Full UL motor capacity |
Woodbury [32] | 93% ischemic | 69.8 ± 11 | 31 days ± 16.88 days | Without reflexes: Severe impairment (0–19 +/− 2), Moderate (19–47 +/− 2), Mild (47–60 +/− 2) |
Michaelsen [33] | n/a | n/a | n/a | With reflexes: Moderate (20–64), Mild (65–66) |
Pang [34] | n/a | n/a | n/a | With reflexes: 0–27, 28–57, 58–66 |
Duncan [35] | Ischemic | 18–90 | >3 | With reflexes: 0–21, 21–50, 51–66 |
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Clark, E. et al. (2019). Brain-Computer Interface for Motor Rehabilitation. In: Stephanidis, C. (eds) HCI International 2019 - Posters. HCII 2019. Communications in Computer and Information Science, vol 1032. Springer, Cham. https://doi.org/10.1007/978-3-030-23522-2_31
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