Informed Bootstrap Augmentation Improves EEG Decoding
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arXiv
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| Auteurs principaux: | , , , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866911267742023680 |
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| author | Jeong, Woojae Cui, Wenhui Avramidis, Kleanthis Medani, Takfarinas Narayanan, Shrikanth Leahy, Richard |
| author_facet | Jeong, Woojae Cui, Wenhui Avramidis, Kleanthis Medani, Takfarinas Narayanan, Shrikanth Leahy, Richard |
| contents | Electroencephalography (EEG) offers detailed access to neural dynamics but remains constrained by noise and trial-by-trial variability, limiting decoding performance in data-restricted or complex paradigms. Data augmentation is often employed to enhance feature representations, yet conventional uniform averaging overlooks differences in trial informativeness and can degrade representational quality. We introduce a weighted bootstrapping approach that prioritizes more reliable trials to generate higher-quality augmented samples. In a Sentence Evaluation paradigm, weights were computed from relative ERP differences and applied during probabilistic sampling and averaging. Across conditions, weighted bootstrapping improved decoding accuracy relative to unweighted (from 68.35% to 71.25% at best), demonstrating that emphasizing reliable trials strengthens representational quality. The results demonstrate that reliability-based augmentation yields more robust and discriminative EEG representations. The code is publicly available at https://github.com/lyricists/NeuroBootstrap. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_12073 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Informed Bootstrap Augmentation Improves EEG Decoding Jeong, Woojae Cui, Wenhui Avramidis, Kleanthis Medani, Takfarinas Narayanan, Shrikanth Leahy, Richard Signal Processing Machine Learning Electroencephalography (EEG) offers detailed access to neural dynamics but remains constrained by noise and trial-by-trial variability, limiting decoding performance in data-restricted or complex paradigms. Data augmentation is often employed to enhance feature representations, yet conventional uniform averaging overlooks differences in trial informativeness and can degrade representational quality. We introduce a weighted bootstrapping approach that prioritizes more reliable trials to generate higher-quality augmented samples. In a Sentence Evaluation paradigm, weights were computed from relative ERP differences and applied during probabilistic sampling and averaging. Across conditions, weighted bootstrapping improved decoding accuracy relative to unweighted (from 68.35% to 71.25% at best), demonstrating that emphasizing reliable trials strengthens representational quality. The results demonstrate that reliability-based augmentation yields more robust and discriminative EEG representations. The code is publicly available at https://github.com/lyricists/NeuroBootstrap. |
| title | Informed Bootstrap Augmentation Improves EEG Decoding |
| topic | Signal Processing Machine Learning |
| url | https://arxiv.org/abs/2511.12073 |