Kessler Roman, Enge Alexander, Skeide Michael A
Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany.
Humboldt-Universität zu Berlin, Berlin, Germany.
Commun Biol. 2025 Jul 10;8(1):1039. doi: 10.1038/s42003-025-08464-3.
Electroencephalography (EEG) preprocessing varies widely between studies, but its impact on classification performance remains poorly understood. To address this gap, we analyzed seven experiments with 40 participants drawn from the public ERP CORE dataset. We systematically varied key preprocessing steps, such as filtering, referencing, baseline interval, detrending, and multiple artifact correction steps, all of which were implemented in MNE-Python. Then we performed trial-wise binary classification (i.e., decoding) using neural networks (EEGNet), or time-resolved logistic regressions. Our findings demonstrate that preprocessing choices influenced decoding performance considerably. All artifact correction steps reduced decoding performance across experiments and models, while higher high-pass filter cutoffs consistently increased decoding performance. For EEGNet, baseline correction further increased decoding performance, and for time-resolved classifiers, linear detrending, and lower low-pass filter cutoffs increased decoding performance. The influence of other preprocessing choices was specific for each experiment or event-related potential component. The current results underline the importance of carefully selecting preprocessing steps for EEG-based decoding. While uncorrected artifacts may increase decoding performance, this comes at the expense of interpretability and model validity, as the model may exploit structured noise rather than the neural signal.
脑电图（EEG）预处理在不同研究中差异很大，但其对分类性能的影响仍知之甚少。为了填补这一空白，我们分析了从公开的ERP CORE数据集中选取的40名参与者的七个实验。我们系统地改变了关键的预处理步骤，如滤波、参考、基线区间、去趋势以及多种伪迹校正步骤，所有这些步骤均在MNE-Python中实现。然后，我们使用神经网络（EEGNet）或时间分辨逻辑回归进行逐次试验的二元分类（即解码）。我们的研究结果表明，预处理选择对解码性能有相当大的影响。所有伪迹校正步骤在各个实验和模型中均降低了解码性能，而较高的高通滤波器截止频率持续提高了解码性能。对于EEGNet，基线校正进一步提高了解码性能，而对于时间分辨分类器，线性去趋势和较低的低通滤波器截止频率提高了解码性能。其他预处理选择的影响因每个实验或事件相关电位成分而异。当前结果强调了为基于EEG的解码仔细选择预处理步骤的重要性。虽然未校正的伪迹可能会提高解码性能，但这是以牺牲可解释性和模型有效性为代价的，因为模型可能利用的是结构化噪声而非神经信号。
