Corticomuscular Co-Activation Based Hybrid Brain-Computer Interface for Motor Recovery Monitoring

Anirban Chowdhury, Ashish Dutta, Girijesh Prasad

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)
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Abstract

The effect of corticomuscular coactivation based hybrid brain-computer interface (h-BCI) on post-stroke neurorehabilitation has not been explored yet. A major challenge in this area is to find an appropriate corticomuscular feature which can not only drive an h-BCI but also serve as a biomarker for motor recovery monitoring. Our previous study established the feasibility of a new method of measuring corticomuscular co-activation called correlation of band-limited power time-courses (CBPT) of EEG and EMG signals, outperforming the traditional EEG-EMG coherence in terms of accurately controlling a robotic hand exoskeleton device by the stroke patients. In this paper, we have evaluated the neurophysiological significance of CBPT for motor recovery monitoring by conducting a 5-week long longitudinal pilot trial on 4 chronic hemiparetic stroke patients. Results show that the CBPT variations correlated significantly (p-value< 0.05) with the dynamic changes in motor outcome measures during the therapy for all the patients. As the bandpower based biomarkers are popular in literature, a comparison with such biomarkers has also been made to cross-verify whether the changes in CBPT are indeed neurophysiological. Thus the study concludes that CBPT can serve as a biomarker for motor recovery monitoring while serving as a corticomuscular co-activation feature for h-BCI based neurorehabilitation. Despite an observed significant positive change between pre- and post-intervention motor outcomes, the question of the clinical effectiveness of CBPT is subject to further controlled trial on a larger cohort.
Original languageEnglish
Pages (from-to)174542 - 174557
Number of pages16
JournalIEEE Access
Volume8
Early online date23 Sep 2020
DOIs
Publication statusE-pub ahead of print - 23 Sep 2020

Keywords

  • Biomarkers
  • brain-computer interfaces
  • electroencephalography
  • electromyography
  • exoskeletons
  • neurofeedback
  • rehabilitation robotics
  • stroke

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