Department of Electrical and Electronic Engineering, University of Cagliari, 09124 Cagliari, Italy.
Department of Mathematics and Computer Science, University of Cagliari, 09124 Cagliari, Italy.
Sensors (Basel). 2022 Apr 8;22(8):2853. doi: 10.3390/s22082853.
M/EEG resting-state analysis often requires the definition of the epoch length and the criteria in order to select which epochs to include in the subsequent steps. However, the effects of epoch selection remain scarcely investigated and the procedure used to (visually) inspect, label, and remove bad epochs is often not documented, thereby hindering the reproducibility of the reported results. In this study, we present Scorepochs, a simple and freely available tool for the automatic scoring of resting-state M/EEG epochs that aims to provide an objective method to aid M/EEG experts during the epoch selection procedure. We tested our approach on a freely available EEG dataset containing recordings from 109 subjects using the BCI2000 64 channel system.
M/EEG 静息态分析通常需要定义时程长度和标准，以便选择要包含在后续步骤中的时程。然而，时程选择的影响仍然很少被研究，并且用于（视觉）检查、标记和删除不良时程的过程通常没有记录，从而阻碍了报告结果的可重复性。在这项研究中，我们提出了 Scorepochs，这是一个简单且免费的用于自动评分静息态 M/EEG 时程的工具，旨在为 M/EEG 专家在时程选择过程中提供一种客观的方法。我们使用 BCI2000 64 通道系统对包含 109 个受试者记录的免费 EEG 数据集测试了我们的方法。
