Abstract
| Language | English |
|---|---|
| Pages | 1265-1275 |
| Number of pages | 10 |
| Journal | IEEE Transactions on Neural Systems and Rehabilitation Engineering |
| Volume | 24 |
| Issue number | 11 |
| Early online date | 7 Apr 2016 |
| DOIs | |
| Publication status | Published - 30 Nov 2016 |
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Keywords
- Active Leg Exoskeleton (ALEX II)
- connectivity analysis
- electroencephalography
- partial Granger causality
- robot-assisted gait training
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Directed Functional Connectivity in Fronto-Centroparietal Circuit Correlates with Motor Adaptation in Gait Training. / Youssofzadeh, Vahab; Zanotto, Damiano; Wong-Lin, KongFatt; Agrawal, Sunil; Prasad, Girijesh; Agrawal, Sunil Kumar.
In: IEEE Transactions on Neural Systems and Rehabilitation Engineering, Vol. 24, No. 11, 30.11.2016, p. 1265-1275.Research output: Contribution to journal › Article
TY - JOUR
T1 - Directed Functional Connectivity in Fronto-Centroparietal Circuit Correlates with Motor Adaptation in Gait Training
AU - Youssofzadeh, Vahab
AU - Zanotto, Damiano
AU - Wong-Lin, KongFatt
AU - Agrawal, Sunil
AU - Prasad, Girijesh
AU - Agrawal, Sunil Kumar
PY - 2016/11/30
Y1 - 2016/11/30
N2 - Lower-extremity robotic exoskeletons are used in gait rehabilitation to achieve functional motor recovery. To date, little is known about how gait training and post-training are characterized in brain signals and their causal connectivity. In this work, we used time-domain partial Granger causality (PGC) analysis to elucidate the directed functional connectivity of electroencephalogram (EEG) signals of healthy adults in robot-assisted gait training (RAGT). Our results confirm the presence of EEG rhythms and corticomuscular relationships during standing and walking using spectral and coherence analyses. The PGC analysis revealed enhanced connectivity close to sensorimotor areas (C3 and CP4) during standing, whereas additional connectivities involve the centroparietal (CPz) and frontal (Fz) areas during walking with respect to standing. In addition, significant fronto-centroparietal causal effects were found during both training and post-training. Strong correlations were also found between kinematic errors and fronto-centroparietal connectivity during training and post-training. This study suggests fronto-centroparietal connectivity as a potential neuromarker for motor learning and adaptation in RAGT.
AB - Lower-extremity robotic exoskeletons are used in gait rehabilitation to achieve functional motor recovery. To date, little is known about how gait training and post-training are characterized in brain signals and their causal connectivity. In this work, we used time-domain partial Granger causality (PGC) analysis to elucidate the directed functional connectivity of electroencephalogram (EEG) signals of healthy adults in robot-assisted gait training (RAGT). Our results confirm the presence of EEG rhythms and corticomuscular relationships during standing and walking using spectral and coherence analyses. The PGC analysis revealed enhanced connectivity close to sensorimotor areas (C3 and CP4) during standing, whereas additional connectivities involve the centroparietal (CPz) and frontal (Fz) areas during walking with respect to standing. In addition, significant fronto-centroparietal causal effects were found during both training and post-training. Strong correlations were also found between kinematic errors and fronto-centroparietal connectivity during training and post-training. This study suggests fronto-centroparietal connectivity as a potential neuromarker for motor learning and adaptation in RAGT.
KW - Active Leg Exoskeleton (ALEX II)
KW - connectivity analysis
KW - electroencephalography
KW - partial Granger causality
KW - robot-assisted gait training
U2 - 10.1109/TNSRE.2016.2551642
DO - 10.1109/TNSRE.2016.2551642
M3 - Article
VL - 24
SP - 1265
EP - 1275
JO - IEEE Transactions on Neural Systems and Rehabilitation Engineering
T2 - IEEE Transactions on Neural Systems and Rehabilitation Engineering
JF - IEEE Transactions on Neural Systems and Rehabilitation Engineering
SN - 1534-4320
IS - 11
ER -