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

TL;DR

This paper presents a novel clustering method based on string compression to extract P300 structures from EEG signals, demonstrating robustness and effectiveness comparable to existing approaches, with potential applications in electrode selection.

Contribution

Introduces a compression-based clustering approach using string transformations and hierarchical methods for P300 EEG signal analysis, addressing variability issues.

Findings

Clustering performance comparable to state-of-the-art methods

Method reveals relevant P300 structures in EEG data

Potential to assist electrode selection for P300 detection

Abstract

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.