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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2502.00220 |
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| _version_ | 1866910810017628160 |
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| author | Sarasa, Guillermo Granados, Ana Rodríguez, Francisco B |
| author_facet | Sarasa, Guillermo Granados, Ana Rodríguez, Francisco B |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_00220 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Algorithmic Clustering based on String Compression to Extract P300 Structure in EEG Signals Sarasa, Guillermo Granados, Ana Rodríguez, Francisco B Machine Learning Information Theory Signal Processing 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. |
| title | Algorithmic Clustering based on String Compression to Extract P300 Structure in EEG Signals |
| topic | Machine Learning Information Theory Signal Processing |
| url | https://arxiv.org/abs/2502.00220 |