The performance of a brain–computer interface (BCI) system is usually measured by its classification accuracy. This creates motivation to increase system accuracies. This article investigates the variation of accuracy values in emotion recognition studies and their relation to cross-validation methods. The literature shows values of accuracies ranging from 60% to 99% while using similar classifiers when tested on the same data set. The study included a literature review of 65 articles testing their algorithms on the DEAP data set up until 2019. Moreover, the study involved the reimplementation of a neural network classifier in both nested and nonnested cross-validation methods. The results show accuracies up to 90% when using nonnested cross validation, while nested cross-validation achieves accuracies around 60%. This article aims to motivate researchers to clearly describe their cross-validation method to avoid confusing other researchers when benchmarking their algorithms.

中文翻译：

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交叉验证方法的批判性分析及其对脑电图分析中神经网络性能膨胀的影响

脑机接口 (BCI) 系统的性能通常通过其分类准确度来衡量。这为提高系统精度创造了动力。本文研究了情绪识别研究中准确度值的变化及其与交叉验证方法的关系。文献显示，当在相同数据集上测试时，使用相似分类器的准确率值范围为 60% 到 99%。该研究包括对截至 2019 年在 DEAP 数据集上测试其算法的 65 篇文章的文献综述。此外，该研究涉及在嵌套和非嵌套交叉验证方法中重新实现神经网络分类器。结果显示，使用非嵌套交叉验证时的准确率高达 90%，而嵌套交叉验证的准确率约为 60%。