This review paper provides an integrated perspective of Explainable Artificial Intelligence techniques applied to Brain-Computer Interfaces. BCIs use predictive models to interpret brain signals for various high-stake applications. However, achieving explainability in these complex models is challenging as it compromises accuracy. The field of XAI has emerged to address the need for explainability across various stakeholders, but there is a lack of an integrated perspective in XAI for BCI (XAI4BCI) literature. It is necessary to differentiate key concepts like explainability, interpretability, and understanding in this context and formulate a comprehensive framework. To understand the need of XAI for BCI, we pose six key research questions for a systematic review and meta-analysis, encompassing its purposes, applications, usability, and technical feasibility. We employ the PRISMA methodology -- preferred reporting items for systematic reviews and meta-analyses to review (n=1246) and analyze (n=84) studies published in 2015 and onwards for key insights. The results highlight that current research primarily focuses on interpretability for developers and researchers, aiming to justify outcomes and enhance model performance. We discuss the unique approaches, advantages, and limitations of XAI4BCI from the literature. We draw insights from philosophy, psychology, and social sciences. We propose a design space for XAI4BCI, considering the evolving need to visualize and investigate predictive model outcomes customised for various stakeholders in the BCI development and deployment lifecycle. This paper is the first to focus solely on reviewing XAI4BCI research articles. This systematic review and meta-analysis findings with the proposed design space prompt important discussions on establishing standards for BCI explanations, highlighting current limitations, and guiding the future of XAI in BCI.

翻译：本综述论文从整合视角探讨了应用于脑机接口的可解释人工智能技术。BCI利用预测模型解读脑信号，服务于多种高风险应用场景。然而，在这些复杂模型中实现可解释性颇具挑战，因其会折损准确性。尽管XAI领域已应运而生以满足不同利益相关方对可解释性的需求，但关于XAI在BCI中应用的整合性视角（XAI4BCI）文献仍显不足。在此背景下，需厘清可解释性、可理解性与理解等关键概念，并构建综合性框架。为深入理解XAI对BCI的必要性，我们提出六项核心研究问题以开展系统综述与元分析，涵盖其目的、应用、可用性及技术可行性。采用PRISMA方法（系统综述与元分析优选报告条目）对2015年及以后发表的1246篇研究进行审阅，并对其中84篇展开深度分析。结果表明，当前研究主要聚焦于面向开发者与研究人员的数据可解释性，旨在验证结果并提升模型性能。我们从文献中归纳XAI4BCI的独特方法、优势与局限，并汲取哲学、心理学及社会科学领域的洞见。针对BCI开发与部署全生命周期中不同利益相关方的可视化与预测模型结果探查需求，提出XAI4BCI设计空间。本文率先聚焦XAI4BCI研究文献的回顾，系统综述与元分析结果结合所提出的设计空间，有力推动BCI解释标准化建立、当前局限揭示及未来XAI在BCI领域发展的讨论。