Algorithmic Clustering based on String Compression to Extract P300 Structure in EEG Signals
This provides a complementary tool for EEG analysis and P300 identification in Brain-Computer Interfaces, though it appears incremental as it builds on existing compression-based techniques.
The paper tackled the challenge of detecting P300 in EEG signals by introducing a clustering method based on Normalized Compression Distance, which achieved performance comparable to state-of-the-art approaches in revealing P300 structures.
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.