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

arXiv: https://arxiv.org/abs/2502.00220 · HTML: https://mlsanity.com/paper/2502.00220/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 0 of its 58 references that are in the map.
- First posted on arXiv 31 Jan 2025.

## Built on by (most cited first, 1 of 1)

- [Context Steering: A New Paradigm for Compression-based Embeddings by Synthesizing Relevant Information Features](https://mlsanity.com/paper/2508.14780/) (2025)

## Abstract (arXiv)

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.