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The feasibility of a brain-computer interface functional electrical stimulation system for the restoration of overground walking after paraplegia.

King CE, Wang PT, McCrimmon CM, Chou CC, Do AH, Nenadic Z - J Neuroeng Rehabil (2015)

Bottom Line: No adverse events directly related to the study were observed.Further studies are warranted to establish the generalizability of these results in a population of individuals with paraplegia due to SCI.In addition, a simplified version of the current system may be explored as a noninvasive neurorehabilitative therapy in those with incomplete motor SCI.

View Article: PubMed Central - PubMed

Affiliation: Department of Neurology, University of California, Los Angeles, CA, USA.

ABSTRACT

Background: Direct brain control of overground walking in those with paraplegia due to spinal cord injury (SCI) has not been achieved. Invasive brain-computer interfaces (BCIs) may provide a permanent solution to this problem by directly linking the brain to lower extremity prostheses. To justify the pursuit of such invasive systems, the feasibility of BCI controlled overground walking should first be established in a noninvasive manner. To accomplish this goal, we developed an electroencephalogram (EEG)-based BCI to control a functional electrical stimulation (FES) system for overground walking and assessed its performance in an individual with paraplegia due to SCI.

Methods: An individual with SCI (T6 AIS B) was recruited for the study and was trained to operate an EEG-based BCI system using an attempted walking/idling control strategy. He also underwent muscle reconditioning to facilitate standing and overground walking with a commercial FES system. Subsequently, the BCI and FES systems were integrated and the participant engaged in several real-time walking tests using the BCI-FES system. This was done in both a suspended, off-the-ground condition, and an overground walking condition. BCI states, gyroscope, laser distance meter, and video recording data were used to assess the BCI performance.

Results: During the course of 19 weeks, the participant performed 30 real-time, BCI-FES controlled overground walking tests, and demonstrated the ability to purposefully operate the BCI-FES system by following verbal cues. Based on the comparison between the ground truth and decoded BCI states, he achieved information transfer rates >3 bit/s and correlations >0.9. No adverse events directly related to the study were observed.

Conclusion: This proof-of-concept study demonstrates for the first time that restoring brain-controlled overground walking after paraplegia due to SCI is feasible. Further studies are warranted to establish the generalizability of these results in a population of individuals with paraplegia due to SCI. If this noninvasive system is successfully tested in population studies, the pursuit of permanent, invasive BCI walking prostheses may be justified. In addition, a simplified version of the current system may be explored as a noninvasive neurorehabilitative therapy in those with incomplete motor SCI.

No MeSH data available.


Related in: MedlinePlus

Virtual Reality Environment. A screenshot of the VRE. The traffic cones next to the characters represent designated stops. A full point was given for dwelling at each designated stop for at least 2 s, for a total stop score of 10 points. A fraction of a point was given for dwelling between 0.5 and 2 s (proportionate to the dwelling time) and no point was given for dwelling less than 0.5 s. There was no penalty for dwelling for more than 2 s, but this increased the course completion time. As a benchmark, the course could be completed in ∼205 s with a manually controlled joystick [15, 16]
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Fig1: Virtual Reality Environment. A screenshot of the VRE. The traffic cones next to the characters represent designated stops. A full point was given for dwelling at each designated stop for at least 2 s, for a total stop score of 10 points. A fraction of a point was given for dwelling between 0.5 and 2 s (proportionate to the dwelling time) and no point was given for dwelling less than 0.5 s. There was no penalty for dwelling for more than 2 s, but this increased the course completion time. As a benchmark, the course could be completed in ∼205 s with a manually controlled joystick [15, 16]

Mentions: Each visit continued with online BCI operation, where 0.75-s-long segments of EEG data were wirelessly acquired in real time every 0.25 s using a sliding window approach. The PSDs of the EEG channels were then calculated and integrated in 2 Hz-bins for each of these segments, and used as the input for the EEG decoding model. The posterior probabilities, and , were calculated using the Bayes rule (see above), and were averaged over a 1.5–2.0 s window to minimize false alarms and omissions [10, 15, 16]. Before online BCI operation, the BCI-VRE system was calibrated using a short procedure (see Additional file 1 for details). During each online experiment, the participant performed between one and five goal-oriented, real-time BCI walking tasks. Specifically, he was instructed to utilize attempted walking and idling to control the linear ambulation of an avatar and make sequential stops at ten designated points within the VRE [14–16]. The goal of the task (see Fig. 1) was to walk the avatar at a constant speed and complete the course as quickly as possible, while dwelling at each stop for at least 2 s. The online performances, expressed as the number of successful stops and course completion time, were compared to the results of Monte Carlo simulations to ascertain whether control of the BCI system was purposeful (details in Additional file 1). Note that despite demonstrating purposeful control during the BCI screening process, the participant continued the BCI-VRE training throughout the study. This provided the EEG decoding model for subsequent BCI-FES experiments. It also allowed the participant’s BCI-VRE performance to be tracked over time and the presumed reactivation of the cortical gait areas to occur.Fig. 1


The feasibility of a brain-computer interface functional electrical stimulation system for the restoration of overground walking after paraplegia.

King CE, Wang PT, McCrimmon CM, Chou CC, Do AH, Nenadic Z - J Neuroeng Rehabil (2015)

Virtual Reality Environment. A screenshot of the VRE. The traffic cones next to the characters represent designated stops. A full point was given for dwelling at each designated stop for at least 2 s, for a total stop score of 10 points. A fraction of a point was given for dwelling between 0.5 and 2 s (proportionate to the dwelling time) and no point was given for dwelling less than 0.5 s. There was no penalty for dwelling for more than 2 s, but this increased the course completion time. As a benchmark, the course could be completed in ∼205 s with a manually controlled joystick [15, 16]
© Copyright Policy - OpenAccess
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC4581411&req=5

Fig1: Virtual Reality Environment. A screenshot of the VRE. The traffic cones next to the characters represent designated stops. A full point was given for dwelling at each designated stop for at least 2 s, for a total stop score of 10 points. A fraction of a point was given for dwelling between 0.5 and 2 s (proportionate to the dwelling time) and no point was given for dwelling less than 0.5 s. There was no penalty for dwelling for more than 2 s, but this increased the course completion time. As a benchmark, the course could be completed in ∼205 s with a manually controlled joystick [15, 16]
Mentions: Each visit continued with online BCI operation, where 0.75-s-long segments of EEG data were wirelessly acquired in real time every 0.25 s using a sliding window approach. The PSDs of the EEG channels were then calculated and integrated in 2 Hz-bins for each of these segments, and used as the input for the EEG decoding model. The posterior probabilities, and , were calculated using the Bayes rule (see above), and were averaged over a 1.5–2.0 s window to minimize false alarms and omissions [10, 15, 16]. Before online BCI operation, the BCI-VRE system was calibrated using a short procedure (see Additional file 1 for details). During each online experiment, the participant performed between one and five goal-oriented, real-time BCI walking tasks. Specifically, he was instructed to utilize attempted walking and idling to control the linear ambulation of an avatar and make sequential stops at ten designated points within the VRE [14–16]. The goal of the task (see Fig. 1) was to walk the avatar at a constant speed and complete the course as quickly as possible, while dwelling at each stop for at least 2 s. The online performances, expressed as the number of successful stops and course completion time, were compared to the results of Monte Carlo simulations to ascertain whether control of the BCI system was purposeful (details in Additional file 1). Note that despite demonstrating purposeful control during the BCI screening process, the participant continued the BCI-VRE training throughout the study. This provided the EEG decoding model for subsequent BCI-FES experiments. It also allowed the participant’s BCI-VRE performance to be tracked over time and the presumed reactivation of the cortical gait areas to occur.Fig. 1

Bottom Line: No adverse events directly related to the study were observed.Further studies are warranted to establish the generalizability of these results in a population of individuals with paraplegia due to SCI.In addition, a simplified version of the current system may be explored as a noninvasive neurorehabilitative therapy in those with incomplete motor SCI.

View Article: PubMed Central - PubMed

Affiliation: Department of Neurology, University of California, Los Angeles, CA, USA.

ABSTRACT

Background: Direct brain control of overground walking in those with paraplegia due to spinal cord injury (SCI) has not been achieved. Invasive brain-computer interfaces (BCIs) may provide a permanent solution to this problem by directly linking the brain to lower extremity prostheses. To justify the pursuit of such invasive systems, the feasibility of BCI controlled overground walking should first be established in a noninvasive manner. To accomplish this goal, we developed an electroencephalogram (EEG)-based BCI to control a functional electrical stimulation (FES) system for overground walking and assessed its performance in an individual with paraplegia due to SCI.

Methods: An individual with SCI (T6 AIS B) was recruited for the study and was trained to operate an EEG-based BCI system using an attempted walking/idling control strategy. He also underwent muscle reconditioning to facilitate standing and overground walking with a commercial FES system. Subsequently, the BCI and FES systems were integrated and the participant engaged in several real-time walking tests using the BCI-FES system. This was done in both a suspended, off-the-ground condition, and an overground walking condition. BCI states, gyroscope, laser distance meter, and video recording data were used to assess the BCI performance.

Results: During the course of 19 weeks, the participant performed 30 real-time, BCI-FES controlled overground walking tests, and demonstrated the ability to purposefully operate the BCI-FES system by following verbal cues. Based on the comparison between the ground truth and decoded BCI states, he achieved information transfer rates >3 bit/s and correlations >0.9. No adverse events directly related to the study were observed.

Conclusion: This proof-of-concept study demonstrates for the first time that restoring brain-controlled overground walking after paraplegia due to SCI is feasible. Further studies are warranted to establish the generalizability of these results in a population of individuals with paraplegia due to SCI. If this noninvasive system is successfully tested in population studies, the pursuit of permanent, invasive BCI walking prostheses may be justified. In addition, a simplified version of the current system may be explored as a noninvasive neurorehabilitative therapy in those with incomplete motor SCI.

No MeSH data available.


Related in: MedlinePlus