Limits...
A Single-Session Preliminary Evaluation of an Affordable BCI-Controlled Arm Exoskeleton and Motor-Proprioception Platform.

Elnady AM, Zhang X, Xiao ZG, Yong X, Randhawa BK, Boyd L, Menon C - Front Hum Neurosci (2015)

Bottom Line: The robotic training device operated to assist a pre-defined goal-directed motor task.Next, we tested the feasibility of robotic training system in individuals with chronic stroke (n = 9) and found that the training device was well tolerated by all the participants.Ability on the motor-proprioception task did not predict the time to completion of the BCI-driven task.

View Article: PubMed Central - PubMed

Affiliation: MENRVA Research Group, School of Engineering Science, Simon Fraser University , Burnaby, BC , Canada.

ABSTRACT
Traditional, hospital-based stroke rehabilitation can be labor-intensive and expensive. Furthermore, outcomes from rehabilitation are inconsistent across individuals and recovery is hard to predict. Given these uncertainties, numerous technological approaches have been tested in an effort to improve rehabilitation outcomes and reduce the cost of stroke rehabilitation. These techniques include brain-computer interface (BCI), robotic exoskeletons, functional electrical stimulation (FES), and proprioceptive feedback. However, to the best of our knowledge, no studies have combined all these approaches into a rehabilitation platform that facilitates goal-directed motor movements. Therefore, in this paper, we combined all these technologies to test the feasibility of using a BCI-driven exoskeleton with FES (robotic training device) to facilitate motor task completion among individuals with stroke. The robotic training device operated to assist a pre-defined goal-directed motor task. Because it is hard to predict who can utilize this type of technology, we considered whether the ability to adapt skilled movements with proprioceptive feedback would predict who could learn to control a BCI-driven robotic device. To accomplish this aim, we developed a motor task that requires proprioception for completion to assess motor-proprioception ability. Next, we tested the feasibility of robotic training system in individuals with chronic stroke (n = 9) and found that the training device was well tolerated by all the participants. Ability on the motor-proprioception task did not predict the time to completion of the BCI-driven task. Both participants who could accurately target (n = 6) and those who could not (n = 3), were able to learn to control the BCI device, with each BCI trial lasting on average 2.47 min. Our results showed that the participants' ability to use proprioception to control motor output did not affect their ability to use the BCI-driven exoskeleton with FES. Based on our preliminary results, we show that our robotic training device has potential for use as therapy for a broad range of individuals with stroke.

No MeSH data available.


Related in: MedlinePlus

Scatter plots of the pre- and post-assessment motor-proprioception assessment results of the stroke participants and the BCI cross-validation accuracy and the time to complete a trial. (A) The scatter plot of the BCI accuracy and the motor-proprioception ability (pre-assessment). (B) The scatter plot of the BCI accuracy and the motor-proprioception ability (post-assessment). (C) The scatter plot of the time taken to complete a trial and the motor-proprioception ability (pre-assessment). (D) The scatter plot of the time taken to complete a trial and the motor-proprioception ability (post-assessment).
© Copyright Policy - open-access
Related In: Results  -  Collection

License
getmorefigures.php?uid=PMC4378300&req=5

Figure 7: Scatter plots of the pre- and post-assessment motor-proprioception assessment results of the stroke participants and the BCI cross-validation accuracy and the time to complete a trial. (A) The scatter plot of the BCI accuracy and the motor-proprioception ability (pre-assessment). (B) The scatter plot of the BCI accuracy and the motor-proprioception ability (post-assessment). (C) The scatter plot of the time taken to complete a trial and the motor-proprioception ability (pre-assessment). (D) The scatter plot of the time taken to complete a trial and the motor-proprioception ability (post-assessment).

Mentions: Figure 7 shows the scatter plot of the motor-proprioception ability and the BCI cross-validation accuracy/Tc. The correlation analysis showed that the participants’ motor-proprioception ability did not have a significant relationship with either the BCI cross-validation accuracy or the time taken to complete a robotic-assisted exercise, Tc. Our preliminary results suggested that the motor-proprioception ability of the stroke individuals and their age and duration of stroke did not affect their ability to use the BCI-driven exoskeleton with FES.


A Single-Session Preliminary Evaluation of an Affordable BCI-Controlled Arm Exoskeleton and Motor-Proprioception Platform.

Elnady AM, Zhang X, Xiao ZG, Yong X, Randhawa BK, Boyd L, Menon C - Front Hum Neurosci (2015)

Scatter plots of the pre- and post-assessment motor-proprioception assessment results of the stroke participants and the BCI cross-validation accuracy and the time to complete a trial. (A) The scatter plot of the BCI accuracy and the motor-proprioception ability (pre-assessment). (B) The scatter plot of the BCI accuracy and the motor-proprioception ability (post-assessment). (C) The scatter plot of the time taken to complete a trial and the motor-proprioception ability (pre-assessment). (D) The scatter plot of the time taken to complete a trial and the motor-proprioception ability (post-assessment).
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4378300&req=5

Figure 7: Scatter plots of the pre- and post-assessment motor-proprioception assessment results of the stroke participants and the BCI cross-validation accuracy and the time to complete a trial. (A) The scatter plot of the BCI accuracy and the motor-proprioception ability (pre-assessment). (B) The scatter plot of the BCI accuracy and the motor-proprioception ability (post-assessment). (C) The scatter plot of the time taken to complete a trial and the motor-proprioception ability (pre-assessment). (D) The scatter plot of the time taken to complete a trial and the motor-proprioception ability (post-assessment).
Mentions: Figure 7 shows the scatter plot of the motor-proprioception ability and the BCI cross-validation accuracy/Tc. The correlation analysis showed that the participants’ motor-proprioception ability did not have a significant relationship with either the BCI cross-validation accuracy or the time taken to complete a robotic-assisted exercise, Tc. Our preliminary results suggested that the motor-proprioception ability of the stroke individuals and their age and duration of stroke did not affect their ability to use the BCI-driven exoskeleton with FES.

Bottom Line: The robotic training device operated to assist a pre-defined goal-directed motor task.Next, we tested the feasibility of robotic training system in individuals with chronic stroke (n = 9) and found that the training device was well tolerated by all the participants.Ability on the motor-proprioception task did not predict the time to completion of the BCI-driven task.

View Article: PubMed Central - PubMed

Affiliation: MENRVA Research Group, School of Engineering Science, Simon Fraser University , Burnaby, BC , Canada.

ABSTRACT
Traditional, hospital-based stroke rehabilitation can be labor-intensive and expensive. Furthermore, outcomes from rehabilitation are inconsistent across individuals and recovery is hard to predict. Given these uncertainties, numerous technological approaches have been tested in an effort to improve rehabilitation outcomes and reduce the cost of stroke rehabilitation. These techniques include brain-computer interface (BCI), robotic exoskeletons, functional electrical stimulation (FES), and proprioceptive feedback. However, to the best of our knowledge, no studies have combined all these approaches into a rehabilitation platform that facilitates goal-directed motor movements. Therefore, in this paper, we combined all these technologies to test the feasibility of using a BCI-driven exoskeleton with FES (robotic training device) to facilitate motor task completion among individuals with stroke. The robotic training device operated to assist a pre-defined goal-directed motor task. Because it is hard to predict who can utilize this type of technology, we considered whether the ability to adapt skilled movements with proprioceptive feedback would predict who could learn to control a BCI-driven robotic device. To accomplish this aim, we developed a motor task that requires proprioception for completion to assess motor-proprioception ability. Next, we tested the feasibility of robotic training system in individuals with chronic stroke (n = 9) and found that the training device was well tolerated by all the participants. Ability on the motor-proprioception task did not predict the time to completion of the BCI-driven task. Both participants who could accurately target (n = 6) and those who could not (n = 3), were able to learn to control the BCI device, with each BCI trial lasting on average 2.47 min. Our results showed that the participants' ability to use proprioception to control motor output did not affect their ability to use the BCI-driven exoskeleton with FES. Based on our preliminary results, we show that our robotic training device has potential for use as therapy for a broad range of individuals with stroke.

No MeSH data available.


Related in: MedlinePlus