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.

本综述论文提供了应用于脑机接口的可解释人工智能技术的整合视角。对大脑信号进行解释的预测模型在各种高风险应用中使用。然而，在这些复杂模型中实现可解释性是具有挑战性的，因为这会损害准确性。为了解决脑机接口的可解释性需求，提出了可解释人工智能为脑机接口的综合框架 (XAI4BCI) 的文献缺乏整合视角的问题。该研究通过系统性回顾和元分析提出了六个关键的研究问题，涵盖了 XAI 在脑机接口的目的、应用、可用性和技术可行性方面。本研究结果强调当前研究主要集中在解释性方面，旨在为开发人员和研究人员提供成果的解释并增强模型性能。研究还从哲学、心理学和社会科学中获取了独特的方法、优势和限制。同时，提出了一种面向 XAI4BCI 的设计空间，考虑到为脑机接口发展和部署生命周期中的各利益相关方定制可视化和研究预测模型结果的不断需求。本论文是第一篇仅专注于回顾 XAI4BCI 研究文章的论文。该系统性回顾和元分析结果与提出的设计空间引发了关于建立 BCI 解释标准的重要讨论，并突出了当前的局限性，指导了 XAI 在 BCI 领域的未来发展。