Informed Bootstrap Augmentation Improves EEG Decoding

• URL: http://arxiv.org/abs/2511.12073v1
• Date: Sat, 15 Nov 2025 07:26:03 GMT
• Title: Informed Bootstrap Augmentation Improves EEG Decoding
• Authors: Woojae Jeong, Wenhui Cui, Kleanthis Avramidis, Takfarinas Medani, Shrikanth Narayanan, Richard Leahy,
• Abstract summary: We introduce a weighted bootstrapping approach that prioritizes more reliable trials to generate higher-quality augmented samples.<n>Across conditions, weighted bootstrapping improved decoding accuracy relative to unweighted (from 68.35% to 71.25% at best)<n>The results demonstrate that reliability-based augmentation yields more robust and discriminative EEG representations.
• Score: 32.32799137951532
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: 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.

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