Informed Bootstrap Augmentation Improves EEG Decoding

TL;DR

This paper introduces a weighted bootstrapping method for EEG decoding that emphasizes more reliable trials, leading to improved accuracy and more robust neural representations in data-limited scenarios.

Contribution

The study presents a novel reliability-based weighted bootstrapping approach for EEG data augmentation, enhancing decoding performance over traditional methods.

Findings

Weighted bootstrapping improved decoding accuracy from 68.35% to 71.25%.

Reliability-based augmentation yields more discriminative EEG representations.

The method is validated in a Sentence Evaluation paradigm.

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.