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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

EEG feature extraction maps. Top: Feature extraction maps obtained by a combination of CPCA and AIDA for classification of attempted walking versus idling. The spatial distribution of features is shown for the frequency bands centered at 15 Hz and 25 Hz, where the features with values close to ±1 are more important for classification. The maps were generated from data collected during the last visit. Bottom: Log power spectral density (PSD) during idling (blue) and walking (green) at electrodes CP3, CPz, and CP4, where shaded regions represent error bars. Underneath the PSD plots are the corresponding signal-to-noise ratio (SNR) plots with significant SNRs (p < 0.01) represented by red lines. Note the event-related synchronization (ERS) in the 13–16 Hz range (at CP3 and CP4) and event-related desynchronization (ERD) in the 23–28 Hz range (at CPz)
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Fig5: EEG feature extraction maps. Top: Feature extraction maps obtained by a combination of CPCA and AIDA for classification of attempted walking versus idling. The spatial distribution of features is shown for the frequency bands centered at 15 Hz and 25 Hz, where the features with values close to ±1 are more important for classification. The maps were generated from data collected during the last visit. Bottom: Log power spectral density (PSD) during idling (blue) and walking (green) at electrodes CP3, CPz, and CP4, where shaded regions represent error bars. Underneath the PSD plots are the corresponding signal-to-noise ratio (SNR) plots with significant SNRs (p < 0.01) represented by red lines. Note the event-related synchronization (ERS) in the 13–16 Hz range (at CP3 and CP4) and event-related desynchronization (ERD) in the 23–28 Hz range (at CPz)

Mentions: The performances achieved during the BCI training procedures are shown in Fig. 4. Note that the Bayesian classifier (2) achieved an offline classification accuracy significantly above the chance level (50 %) on the second visit, and a near-perfect classification accuracy by the 15th visit. This translated into a near-perfect level of control during the goal-oriented real-time BCI walking task within the VRE (Additional file 2), which is evident by the decrease in mean course completion time and increase in successful stop score. The EEG decoding models resulted in spatio-spectral features that converged to similar frequencies and brain areas across visits (see Additional file 1). A sample of these features is depicted in Fig. 5, where areas under electrodes CP3, CPz, and CP4 were deemed by the decoding model as important for classification of attempted walking and idling. Note that these areas approximately correspond to the motor and somatosensory cortices. Spectral analysis confirmed the physiological basis of these features, as event-related synchronization (ERS) was observed at CP3 and CP4 in the low- β band (13 – 16 Hz), and event-related desynchronization (ERD) was observed at CPz in the high- β band (23 – 28 Hz).Fig. 4


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)

EEG feature extraction maps. Top: Feature extraction maps obtained by a combination of CPCA and AIDA for classification of attempted walking versus idling. The spatial distribution of features is shown for the frequency bands centered at 15 Hz and 25 Hz, where the features with values close to ±1 are more important for classification. The maps were generated from data collected during the last visit. Bottom: Log power spectral density (PSD) during idling (blue) and walking (green) at electrodes CP3, CPz, and CP4, where shaded regions represent error bars. Underneath the PSD plots are the corresponding signal-to-noise ratio (SNR) plots with significant SNRs (p < 0.01) represented by red lines. Note the event-related synchronization (ERS) in the 13–16 Hz range (at CP3 and CP4) and event-related desynchronization (ERD) in the 23–28 Hz range (at CPz)
© Copyright Policy - OpenAccess
Related In: Results  -  Collection

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

Fig5: EEG feature extraction maps. Top: Feature extraction maps obtained by a combination of CPCA and AIDA for classification of attempted walking versus idling. The spatial distribution of features is shown for the frequency bands centered at 15 Hz and 25 Hz, where the features with values close to ±1 are more important for classification. The maps were generated from data collected during the last visit. Bottom: Log power spectral density (PSD) during idling (blue) and walking (green) at electrodes CP3, CPz, and CP4, where shaded regions represent error bars. Underneath the PSD plots are the corresponding signal-to-noise ratio (SNR) plots with significant SNRs (p < 0.01) represented by red lines. Note the event-related synchronization (ERS) in the 13–16 Hz range (at CP3 and CP4) and event-related desynchronization (ERD) in the 23–28 Hz range (at CPz)
Mentions: The performances achieved during the BCI training procedures are shown in Fig. 4. Note that the Bayesian classifier (2) achieved an offline classification accuracy significantly above the chance level (50 %) on the second visit, and a near-perfect classification accuracy by the 15th visit. This translated into a near-perfect level of control during the goal-oriented real-time BCI walking task within the VRE (Additional file 2), which is evident by the decrease in mean course completion time and increase in successful stop score. The EEG decoding models resulted in spatio-spectral features that converged to similar frequencies and brain areas across visits (see Additional file 1). A sample of these features is depicted in Fig. 5, where areas under electrodes CP3, CPz, and CP4 were deemed by the decoding model as important for classification of attempted walking and idling. Note that these areas approximately correspond to the motor and somatosensory cortices. Spectral analysis confirmed the physiological basis of these features, as event-related synchronization (ERS) was observed at CP3 and CP4 in the low- β band (13 – 16 Hz), and event-related desynchronization (ERD) was observed at CPz in the high- β band (23 – 28 Hz).Fig. 4

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