Integrating EEG and EMG data: a novel statistical pipeline for investigating brain-muscle interaction in experimental neuroarchaeology.

Abstract

This study introduces a novel multivariate statistical pipeline for integrating and analyzing EEG and EMG data in experimental neuroarchaeology, providing a robust framework for investigating brain-hand interactions during a variety of tool-related behaviours. By implementing a multistep approach, it effectively characterizes task/condition-related variations while detecting meaningful covariation patterns between neural and muscular activity. Compared to traditional univariate techniques, this pipeline better addresses the complexity of multimodal datasets, offering a more comprehensive understanding of cognitive-motor dynamics. We demonstrate the utility of this approach using data from a previously conducted experiment on early hominin stone tool use, illustrating how electroencephalography (EEG) and electromyography (EMG) integration can reveal interactions between brain and hand processes across tasks of varying manual complexity. This methodological advancement not only enhances the study of tool-related behaviors but also establishes a standardized framework for future research in neuroarchaeology. Beyond stone tools, this approach could be extended to other forms of material culture, such as ornaments and engravings, contributing to a broader understanding of manual skill development and symbolic behavior.