IEEE Transactions on Neural Systems and Rehabilitation Engineering (Jan 2024)

Enhancing ERD Activation and Functional Connectivity via the Sixth-Finger Motor Imagery in Stroke Patients

  • Zhuang Wang,
  • Yuan Liu,
  • Shuaifei Huang,
  • Huimin Huang,
  • Wenlai Wu,
  • Yuyang Wang,
  • Xingwei An,
  • Dong Ming

DOI
https://doi.org/10.1109/TNSRE.2024.3486551
Journal volume & issue
Vol. 32
pp. 3902 – 3912

Abstract

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Motor imagery (MI) is widely employed in stroke rehabilitation due to the event-related desynchronization (ERD) phenomenon in sensorimotor cortex induced by MI is similar to actual movement. However, the traditional BCI paradigm, in which the patient imagines the movement of affected hand (AH-MI) with a weak ERD caused by the damaged brain regions, retards motor relearning process. In this work, we applied a novel MI paradigm based on the “sixth-finger” (SF-MI) in stroke patients and systematically uncovered the ERD pattern enhancement of novel MI paradigm compared to traditional MI paradigm. Twenty stroke patients were recruited for this experiment. Event-related spectral perturbation was adopted to supply details about ERD. Brain activation region, intensity and functional connectivity were compared between SF-MI and AH-MI to reveal the ERD enhancement performance of novel MI paradigm. A “wider range, stronger intensity, greater connection” ERD activation pattern was induced in stroke patients by novel SF-MI paradigm compared to traditional AH-MI paradigm. The bilateral sensorimotor and prefrontal modulation was found in SF-MI, which was different in AH-MI only weak sensorimotor modulation was exhibited. The ERD enhancement is mainly concentrated in mu rhythm. More synchronized and intimate neural activity between different brain regions was found during SF-MI tasks compared to AH-MI tasks. Classification results (>80% in SF-MI vs. REST) also indicated the feasibility of applying novel MI paradigm to clinical stroke rehabilitation. This work provides a novel MI paradigm and demonstrates its neural activation-enhancing performance, helping to develop more effective MI-based BCI system for stroke rehabilitation.

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