CEP-MI: a novel cue-execution dual-stage motor imagery paradigm with PGCANet decoding architecture
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Published on: May 10, 2024
Jianxiu Li1,2, Pengda Yu1,2,3, Huiyu Qiao1,2
1School of Electronic Information Engineering, Inner Mongolia University, Hohhot, China.
Background:
Motor imagery-based brain-computer interface (MI-BCI) holds great potential in neurorehabilitation and motor assistance. Data quality and motor intention decoding effectiveness are closely associated with participants' engagement. However, previous MI tasks ignored active engagement regulation, and neural mechanisms underlying the transition from motor imagery to execution remain unclear, with limited relevant research. Conventional separate-trial designs cannot capture continuous brain dynamics from imagery to execution and cannot objectively evaluate subject engagement, which hinders mechanism exploration and stable decoding.
Methods:
To address these paradigm-related limitations, a novel cue-execution MI paradigm (CEP-MI) with countdown anchors was designed to quantify engagement. Motor execution sessions were added post-imagery to reveal neural differences during the transition and identify movement-related brain activities. Meanwhile, to boost MI-EEG decoding performance, a collaborative decoding architecture (PGCANet) integrating prior guidance and channel awareness was proposed, with dual branches and consistency constraint loss for decision fusion.
Results:
CEP-MI effectively quantifies participant engagement, and PGCANet achieves superior decoding performance compared to traditional algorithms and advanced models, with the optimal average accuracy reaching 85.77% in binary classification and 82.47% in quadruple classification.
Conclusion:
Targeting the above-mentioned research gaps, the CEP-MI paradigm provides valuable insights into the imagery-execution transition mechanism. PGCANet enhances MI decoding accuracy via multi-branch collaboration, laying a foundation for MI-BCI's clinical application in neurorehabilitation.
Significance:
This study introduces a novel paradigm and collaborative architecture that significantly advance MI-BCI decoding accuracy and engagement quantification, and helps to promote its clinical application in neurorehabilitation.
