提出新方法解决脑电听觉注意解码中数据不平衡导致的性能虚高问题。
Decoding Stimulus Reconstruction-Based Auditory Attention Robustly in Unbalanced EEG Datasets
- 设计LOPEO交叉验证，避免在不平衡数据上过度乐观评估性能。
- 实验显示传统方法在不平衡数据上解码准确率被显著高估。
- 适合关注脑电信号解码可靠性与实验设计的研究者。
过去十年，大量研究采用深度神经网络（DNN）通过刺激重构法从脑电图（EEG）信号中解码听觉注意（AAD）。然而，数据集平衡性对基于刺激重构的AAD解码性能的影响尚未被探索。本研究使用三个公开可用的EEG-AAD数据集——KUL、DTU和NJU cEEGrid——构建平衡与不平衡的实验条件。我们假设并证实，基于刺激重构的DNN解码器在不平衡数据集上倾向于产生过高的解码性能。为解决此问题，我们提出一种留一配对包络排除（LOPEO）交叉验证协议。实验结果表明，LOPEO能有效防止在不平衡数据集上出现性能虚高。尽管平衡数据集在实验设计中更优，但LOPEO为已发布的不平衡数据集提供了严谨的评估框架，填补了该领域的关键空白。
原文摘要 · Abstract (English)
In the past decade, numerous studies have applied deep neural networks (DNNs) to decode auditory attention (AAD) from Electroencephalogram (EEG) signals via stimulus reconstruction. However, the influence of dataset balance on the decoding performance of stimulus reconstruction-based AAD remains unexplored. In this study, three publicly available EEG-AAD datasets - KUL, DTU, and NJU cEEGrid - are used to construct both balanced and unbalanced experimental conditions. We hypothesize and demonstrate that stimulus reconstruction-based DNN decoders tend to produce overestimated decoding performance on unbalanced datasets. To address this issue, we propose a leave-one-paired-envelope-out (LOPEO) cross-validation protocol. Experimental results confirm that LOPEO effectively prevents inflated decoding accuracy on unbalanced datasets. While balanced datasets are generally preferred in experimental design, LOPEO provides a principled evaluation framework for unbalanced datasets that have already been published, filling an important gap in the field.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。