Does Re-referencing Matter? Large Laplacian Filter Optimizes Single-Trial P300 BCI Performance
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arXiv
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866909840403595264 |
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| author | Guttmann-Flury, Eva Zhao, Jian Sawan, Mohamad |
| author_facet | Guttmann-Flury, Eva Zhao, Jian Sawan, Mohamad |
| contents | Electroencephalography (EEG) provides a non-invasive window into brain activity, enabling Brain-Computer Interfaces (BCIs) for communication and control. However, their performance is limited by signal fidelity issues, among which the choice of re-referencing strategy is a pervasive but often overlooked preprocessing bias. Addressing controversies about its necessity and optimal choice, we adopted a quantified approach to evaluate four strategies - no re-referencing, Common Average Reference (CAR), small Laplacian, and large Laplacian - using 62-channels EEG (31 subjects, 2,520 trials). To our knowledge, this is the first study systematically quantifying their impact on single-trial P300 classification accuracy. Our controlled pipeline isolated re-referencing effects for source-space reconstruction (eLORETA with Phase Lag Index) and anatomically constrained classification. The large Laplacian resolves distributed P3b networks while maintaining P3a specificity, achieving the best P300 peak classification accuracy (81.57% hybrid method; 75.97% majority regions of interest). Performance follows a consistent and statistically significant hierarchy: large Laplacian > CAR > no re-reference > small Laplacian, providing a foundation for unified methodological evaluation. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_10733 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Does Re-referencing Matter? Large Laplacian Filter Optimizes Single-Trial P300 BCI Performance Guttmann-Flury, Eva Zhao, Jian Sawan, Mohamad Neurons and Cognition Quantitative Methods Electroencephalography (EEG) provides a non-invasive window into brain activity, enabling Brain-Computer Interfaces (BCIs) for communication and control. However, their performance is limited by signal fidelity issues, among which the choice of re-referencing strategy is a pervasive but often overlooked preprocessing bias. Addressing controversies about its necessity and optimal choice, we adopted a quantified approach to evaluate four strategies - no re-referencing, Common Average Reference (CAR), small Laplacian, and large Laplacian - using 62-channels EEG (31 subjects, 2,520 trials). To our knowledge, this is the first study systematically quantifying their impact on single-trial P300 classification accuracy. Our controlled pipeline isolated re-referencing effects for source-space reconstruction (eLORETA with Phase Lag Index) and anatomically constrained classification. The large Laplacian resolves distributed P3b networks while maintaining P3a specificity, achieving the best P300 peak classification accuracy (81.57% hybrid method; 75.97% majority regions of interest). Performance follows a consistent and statistically significant hierarchy: large Laplacian > CAR > no re-reference > small Laplacian, providing a foundation for unified methodological evaluation. |
| title | Does Re-referencing Matter? Large Laplacian Filter Optimizes Single-Trial P300 BCI Performance |
| topic | Neurons and Cognition Quantitative Methods |
| url | https://arxiv.org/abs/2510.10733 |