<ul><li>A structured and sparse partial least squares coherence algorithm (ssPLSC) has been proposed for multivariate cortico-muscular analysis.</li><li>The ssPLSC approach addresses challenges such as high dimensionality, limited sample sizes, and the need for interpretability and spatial structure.</li><li>An efficient alternating iterative algorithm has been developed to solve the optimization problem in ssPLSC and its convergence has been proven experimentally.</li><li>Experimental results have demonstrated that ssPLSC outperforms representative multivariate cortico-muscular fusion methods in scenarios with limited sample sizes and high noise levels, making it a transformative tool for evaluating corticospinal pathway integrity in neurological disorders.</li></ul>

Structured and sparse partial least squares coherence for multivariate cortico-muscular analysis

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