Directed neural connectivity changes in robot-assisted gait training: A partial Granger causality analysis

Vahab Youssofzadeh, Damiano Zanotto, Paul Stegall, Muhammad Naeem, KongFatt Wong-Lin, Sunil Agrawal, G Prasad

Research output: Chapter in Book/Report/Conference proceedingConference contribution

7 Citations (Scopus)

Abstract

Now-a-days robotic exoskeletons are often used to help in gait training of stroke patients. However, such robotic systems have so far yielded only mixed results in benefiting the clinical population. Therefore, there is a need to investigate how gait learning and de-learning get characterised in brain signals and thus determine neural substrate to focus attention on, possibly, through an appropriate brain-computer interface (BCI). To this end, this paper reports the analysis of EEG data acquired from six healthy individuals undergoing robot-assisted gait training of a new gait pattern. Time-domain partial Granger causality (PGC) method was applied to estimate directed neural connectivity among relevant brain regions. To validate the results, a power spectral density (PSD) analysis was also performed. Results showed a strong causal interaction between lateral motor cortical areas. A fronto-parietal connection was found in all robot-assisted training sessions. Following training, a causal “top-down” cognitive control was evidenced, which may indicate plasticity in the connectivity in the respective brain regions.
LanguageEnglish
Title of host publicationUnknown Host Publication
Pages6361-6364
Number of pages4
DOIs
Publication statusPublished - 2014
Event36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society - Sheraton Chicago Hotel and Towers, Chicago, Illinois, USA
Duration: 1 Jan 2014 → …

Conference

Conference36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society
Period1/01/14 → …

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Gait
Causality
Brain
Learning
Brain-Computer Interfaces
Motor Cortex
Robotics
Electroencephalography
Stroke
Population

Cite this

Youssofzadeh, V., Zanotto, D., Stegall, P., Naeem, M., Wong-Lin, K., Agrawal, S., & Prasad, G. (2014). Directed neural connectivity changes in robot-assisted gait training: A partial Granger causality analysis. In Unknown Host Publication (pp. 6361-6364) https://doi.org/10.1109/EMBC.2014.6945083
Youssofzadeh, Vahab ; Zanotto, Damiano ; Stegall, Paul ; Naeem, Muhammad ; Wong-Lin, KongFatt ; Agrawal, Sunil ; Prasad, G. / Directed neural connectivity changes in robot-assisted gait training: A partial Granger causality analysis. Unknown Host Publication. 2014. pp. 6361-6364
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abstract = "Now-a-days robotic exoskeletons are often used to help in gait training of stroke patients. However, such robotic systems have so far yielded only mixed results in benefiting the clinical population. Therefore, there is a need to investigate how gait learning and de-learning get characterised in brain signals and thus determine neural substrate to focus attention on, possibly, through an appropriate brain-computer interface (BCI). To this end, this paper reports the analysis of EEG data acquired from six healthy individuals undergoing robot-assisted gait training of a new gait pattern. Time-domain partial Granger causality (PGC) method was applied to estimate directed neural connectivity among relevant brain regions. To validate the results, a power spectral density (PSD) analysis was also performed. Results showed a strong causal interaction between lateral motor cortical areas. A fronto-parietal connection was found in all robot-assisted training sessions. Following training, a causal “top-down” cognitive control was evidenced, which may indicate plasticity in the connectivity in the respective brain regions.",
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Youssofzadeh, V, Zanotto, D, Stegall, P, Naeem, M, Wong-Lin, K, Agrawal, S & Prasad, G 2014, Directed neural connectivity changes in robot-assisted gait training: A partial Granger causality analysis. in Unknown Host Publication. pp. 6361-6364, 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 1/01/14. https://doi.org/10.1109/EMBC.2014.6945083

Directed neural connectivity changes in robot-assisted gait training: A partial Granger causality analysis. / Youssofzadeh, Vahab; Zanotto, Damiano; Stegall, Paul; Naeem, Muhammad; Wong-Lin, KongFatt; Agrawal, Sunil; Prasad, G.

Unknown Host Publication. 2014. p. 6361-6364.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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AB - Now-a-days robotic exoskeletons are often used to help in gait training of stroke patients. However, such robotic systems have so far yielded only mixed results in benefiting the clinical population. Therefore, there is a need to investigate how gait learning and de-learning get characterised in brain signals and thus determine neural substrate to focus attention on, possibly, through an appropriate brain-computer interface (BCI). To this end, this paper reports the analysis of EEG data acquired from six healthy individuals undergoing robot-assisted gait training of a new gait pattern. Time-domain partial Granger causality (PGC) method was applied to estimate directed neural connectivity among relevant brain regions. To validate the results, a power spectral density (PSD) analysis was also performed. Results showed a strong causal interaction between lateral motor cortical areas. A fronto-parietal connection was found in all robot-assisted training sessions. Following training, a causal “top-down” cognitive control was evidenced, which may indicate plasticity in the connectivity in the respective brain regions.

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Youssofzadeh V, Zanotto D, Stegall P, Naeem M, Wong-Lin K, Agrawal S et al. Directed neural connectivity changes in robot-assisted gait training: A partial Granger causality analysis. In Unknown Host Publication. 2014. p. 6361-6364 https://doi.org/10.1109/EMBC.2014.6945083