Electroencephalography (EEG) data provides a non-invasive method for researchers and clinicians to observe brain activity in real time. The integration of deep learning techniques with EEG data has significantly improved the ability to identify meaningful patterns, leading to valuable insights for both clinical and research purposes. However, most of the frameworks so far, designed for EEG data analysis, are either too focused on pre-processing or in deep learning methods per, making their use for both clinician and developer communities problematic. Moreover, critical issues such as ethical considerations, biases, uncertainties, and the limitations inherent in AI models for EEG data analysis are frequently overlooked, posing challenges to the responsible implementation of these technologies. In this paper, we introduce a comprehensive deep learning framework tailored for EEG data processing, model training and report generation. While constructed in way to be adapted and developed further by AI developers, it enables to report, through model cards, the outcome and specific information of use for both developers and clinicians. In this way, we discuss how this framework can, in the future, provide clinical researchers and developers with the tools needed to create transparent and accountable AI models for EEG data analysis and diagnosis.

翻译：脑电图（EEG）数据为研究人员和临床医生提供了一种非侵入性的实时观察大脑活动的方法。深度学习技术与EEG数据的结合显著提升了识别有意义模式的能力，从而为临床和研究目的带来了宝贵的见解。然而，迄今为止大多数专为EEG数据分析设计的框架，要么过于侧重预处理，要么仅专注于深度学习方法本身，这使其在临床医生和开发者群体中的应用均存在问题。此外，伦理考量、偏见、不确定性以及AI模型在EEG数据分析中固有的局限性等关键问题常被忽视，对这些技术的负责任实施构成了挑战。本文介绍了一个专为EEG数据处理、模型训练和报告生成而设计的综合性深度学习框架。该框架在构建时考虑了AI开发者的适应和进一步开发需求，同时能够通过模型卡为开发者和临床医生报告结果及具体使用信息。通过这种方式，我们探讨了该框架未来如何为临床研究者和开发者提供必要的工具，以创建透明且可问责的、用于EEG数据分析和诊断的AI模型。