Mapping the literature about brain-computer interface in rehabilitation: a graph-theory-based PCA framework for semantic space analysis

Brain-Computer Interfaces (BCIs) are increasingly used in neurorehabilitation, but the rapid expansion of scientific literature complicates the identification of clinically relevant studies. This study investigated whether expert-defined relevance within BCI rehabilitation literature emerges as a structural property of semantic networks through the integration of graph theory and Principal Component Analysis (PCA). A Lexical Network Analysis Based on Graph Theory (LENGTH) was applied to randomized controlled trials indexed in PubMed over the last decade using the query “brain computer interface” AND rehabilitation. Titles and abstracts were analyzed to construct a semantic network linking articles and lexical terms. Multiple graph-theoretical metrics were calculated and residualized against weighted degree to minimize document-size bias. PCA was subsequently applied to the residualized metrics. Forty-eight studies were included. The network showed a compact and highly interconnected structure, centered on motor and functional recovery concepts. PCA identified two principal components explaining of total variance. Relevant articles tended to occupy regions characterized by higher semantic integration and lower hierarchical influence. Although no clear categorical separation emerged, a consistent positional tendency was observed. These findings suggest that relevance may be represented as a topological property within a multidimensional semantic landscape, supporting the use of semantic-network approaches for literature screening and evidence synthesis.

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