Abstract
Conventional passive lower limb rehabilitation is suboptimal since the brain is not actively involved in the training. An autonomous motor imagery brain-computer interface (MI-BCI) could potentially improve rehabilitation outcomes. However, motor cortex regions associated with the individual feet are anatomically close to each other. This presents a difficulty in distinguishing the left and right foot MI during rehabilitation therapy. To overcome this difficulty, we extracted functional connectivity to measure the global cortical network via electroencephalography (EEG) signals. Fourteen spatial connections (P3-Fp1, P3-F3, P3-F7, P3-C3, T5-F7, T5-C3, T5-T3, Fp2-T5, Fp2-P3, T6-Fp2, T6-T4, Cz-Fp1, Cz-F7 and Fp2-F7) found across twelve subjects significantly differed between the left and right foot MI, evidencing nonlocalized brain activity during MI. Foot MI were distinguished using machine learning algorithms in terms of the time- and frequency-domain connectivities extracted from Pearson's correlation, multivariate autoregression (MVAR), bandpass correlation, and partial directed coherence (PDC) models. The results showed that connectivity extracted by pairwise Pearson's correlation could be distinguished with 86.26 ± 9.95%, while in the frequency-domain, the gamma band presented the best classification accuracy of 73.55 ± 17.11%. We attempted to simulate asynchronous real-time classification paradigms in order to evaluate the classification performance of connectivity features compared to common spatial pattern (CSP) and band power (BP). The results indicate correlation-connectivity has the best outcome, attaining an accuracy of 80.75 ± 9.51% in asynchronous classification.
Highlights
Rehabilitation after stroke aims to promote neuron plasticity in the affected motor cortex
It is interesting to note that there are numerous significant positive and negative connections observed across the subjects, indicating that foot motor imagery (MI) could potentially be discriminated via the global cortical network
We evaluated the classification performance of connectivity features compared to benchmark motor imagery brain-computer interface (MI-Brain-computer interfaces (BCIs)) features, such as band power (BP) and common spatial pattern (CSP), in a simulated asynchronous experiment
Summary
Rehabilitation after stroke aims to promote neuron plasticity in the affected motor cortex. Conventional lower limb rehabilitation usually requires multiple therapists to manually assist patients in moving their foot. This passive therapy might fail to produce the desired outcome since the brain is not actively involved in the training. The associate editor coordinating the review of this manuscript and approving it for publication was Bin Liu. Active training with autonomous involvement has been made possible by monitoring electromyography (EMG) signals in the affected foot. Recent interest in upper limb rehabilitation has included electroencephalography (EEG) monitoring, including the proposal of several motor imagery (MI) based rehabilitation systems [6]–[8]. The recently proposed lower limb rehabilitation systems were binary (rest vs imagery) [10], [11]. Bipedal MI training was near to impossible because the foot motor regions are close to each other
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
