Abstract：
Objective.This study aims to address the challenges associated with data-driven electroencephalography (EEG) data analysis by introducing a standardised library calledBIDSAlign. This library efficiently processes and merges heterogeneous EEG datasets from different sources into a common standard template. The goal of this work is to create an environment that allows to preprocess public datasets in order to provide data for the effective training of deep learning (DL) architectures.Approach.The library can handle both Brain Imaging Data Structure (BIDS) and non-BIDS datasets, allowing the user to easily preprocess multiple public datasets. It unifies the EEG recordings acquired with different settings by defining a common pipeline and a specified channel template. An array of visualisation functions is provided inside the library, together with a user-friendly graphical user interface to assist non-expert users throughout the workflow.Main results.BIDSAlign enables the effective use of public EEG datasets, providing valuable medical insights, even for non-experts in the field. Results from applying the library to datasets from OpenNeuro demonstrate its ability to extract significant medical knowledge through an end-to-end workflow, facilitating group analysis, visual comparison and statistical testing.Significance.BIDSAlign solves the lack of large EEG datasets by aligning multiple datasets to a standard template. This unlocks the potential of public EEG data for training DL models. It paves the way to promising contributions based on DL to clinical and non-clinical EEG research, offering insights that can inform neurological disease diagnosis and treatment strategies.
摘要：
目的：本研究旨在通过引入称为BIDSAlign的标准化库，解决与数据驱动的脑电图（EEG）数据分析相关的挑战。该库可有效地处理来自不同来源的异构EEG数据集并将其合并为通用标准模板。这项工作的目标是创建一个允许预处理公共数据集的环境，以便为深度学习架构的有效训练提供数据。
方法。该库可以处理BIDS（脑成像数据结构）和非BIDS数据集，允许用户轻松预处理多个公共数据集。它通过定义公共管道和指定的通道模板来统一使用不同设置获取的EEG记录。库内部提供了一系列可视化函数，以及用户友好的GUI，以在整个工作流程中帮助非专家用户。
主要结果。BIDSAlign可以有效地使用公共EEG数据集，提供有价值的医学见解，即使是该领域的非专家。将库应用于OpenNeuro的数据集的结果证明了其通过端到端工作流程提取重要医学知识的能力，促进群体分析，视觉比较和统计检验。
意义。BIDSAlign通过将多个数据集与标准模板对齐来解决大型EEG数据集的不足。这释放了用于训练深度学习模型的公共EEG数据的潜力。它为基于深度学习对临床和非临床脑电图研究的有希望的贡献铺平了道路，提供可以为神经系统疾病诊断和治疗策略提供信息的见解。
