Abstract
Compared with the efficacy of traditional physical therapy, a new therapy utilizing motor imagery can induce brain plasticity and allows partial recovery of motor ability in patients with hemiplegia after stroke. Here, we proposed an updated paradigm utilizing motor coordination imagery involving the lower limbs (normal gait imagery and hemiplegic gait imagery after stroke) and decoded such imagery via an electroencephalogram- (EEG-) based brain network. Thirty subjects were recruited to collect EEGs during motor coordination imagery involving the lower limbs. Time-domain analysis, power spectrum analysis, time-frequency analysis, brain network analysis, and statistical analysis were used to explore the neural mechanisms of motor coordination imagery involving the lower limbs. Then, EEG-based brain network features were extracted, and a support vector machine was used for decoding. The results showed that the two employed motor coordination imageries mainly activated sensorimotor areas; the frequency band power was mainly concentrated within theta and alpha bands, and brain functional connections mainly occurred in the right forehead. The combination of the network attributes of the EEG-based brain network and the spatial features of the adjacency matrix had good separability for the two kinds of gait imagery (p < 0.05), and the average classification accuracy of the combination feature was 92.96% ± 7.54%. Taken together, our findings suggest that brain network features can be used to identify normal gait imagery and hemiplegic gait imagery after stroke.
Highlights
45% of poststroke hemiplegic patients depend on wheelchairs to move [1,2,3,4]
In terms of analysis content, previous studies have mainly focused on the control of redundant degrees of freedom, quantification of motor coordination, external environments, and evaluation of motor coordination, while our present study focused on the neural mechanisms and decoding of motor coordination imagery involving the lower limb
Statistical analysis showed that there was a significant difference in the characteristic path lengths between motor coordination imagery (MCI) and REST, for which only the O2 channel was not statistically significant. ere was no significant difference between the characteristic path lengths between imaging exceptional gait (IEG) and REST, whereas the p values of P3, C3, CP4, and Pz channels of IEG were small, but the difference between MCI and IEG was significant. is result may be due to the fact that imagining MCI and REST is a relatively easy psychological activity, while imagining IEG is a relatively difficult psychological activity
Summary
45% of poststroke hemiplegic patients depend on wheelchairs to move [1,2,3,4]. Traditional physical therapy has only a limited effect on rehabilitation of the lower limbs of poststroke hemiplegic patients [5]. Motor imagery therapy can induce plasticity in the brain [6,7,8,9,10], which can partially restore lower limb movement in poststroke hemiplegic patients [11,12,13,14]. Most motor imagery therapies have focused on improving the performance of the upper limbs [15, 16], whereas only a few studies have aimed at improving performance of the lower limbs [17]. Few studies have investigated the neural mechanisms of motor coordination imagery, and there has not been much focus on decoding motor coordination imagery involving the lower limbs
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