Algorithmic Clustering based on String Compression to Extract P300 Structure in EEG Signals

• Main Authors: Sarasa, Guillermo, Granados, Ana, Rodríguez, Francisco B
• Format: Journal Article
• Language: English
• Published: 31.01.2025
• Subjects: Computer Science - Information Theory; Computer Science - Learning; Mathematics - Information Theory
• DOI: 10.48550/arxiv.2502.00220

Computer Methods and Programs in Biomedicine 2019 P300 is an Event-Related Potential widely used in Brain-Computer Interfaces, but its detection is challenging due to inter-subject and temporal variability. This work introduces a clustering methodology based on Normalized Compression Distance (NCD) to extract the P300 structure, ensuring robustness against variability. We propose a novel signal-to-ASCII transformation to generate compression-friendly objects, which are then clustered using a hierarchical tree-based method and a multidimensional projection approach. Experimental results on two datasets demonstrate the method's ability to reveal relevant P300 structures, showing clustering performance comparable to state-of-the-art approaches. Furthermore, analysis at the electrode level suggests that the method could assist in electrode selection for P300 detection. This compression-driven clustering methodology offers a complementary tool for EEG analysis and P300 identification.