Explainable artificial intelligence approaches for brain-computer interfaces: a review and design space.

Param Rajpura, Hubert Cecotti, Yogesh Kumar Meena
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Abstract

Objective.This review paper provides an integrated perspective of Explainable Artificial Intelligence (XAI) techniques applied to Brain-Computer Interfaces (BCIs). 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. Trust in these models can be established by incorporating reasoning or causal relationships from domain experts. 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, often used interchangeably in this context, and formulate a comprehensive framework.Approach.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 analyse (n = 84) studies published in 2015 and onwards for key insights.Main results.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 visualise and investigate predictive model outcomes customised for various stakeholders in the BCI development and deployment lifecycle.Significance.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.

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用于脑机接口的可解释人工智能方法:回顾与设计空间。
目的:这篇综述论文从综合角度阐述了应用于脑机接口(BCIs)的可解释人工智能(XAI)技术。BCIs 使用预测模型来解释大脑信号,用于各种高风险应用。然而,在这些复杂的模型中实现可解释性具有挑战性,因为这会影响准确性。可以通过纳入领域专家的推理或因果关系来建立对这些模型的信任。为了满足不同利益相关者对可解释性的需求,XAI 领域应运而生,但在 XAI for BCI(XAI4BCI)文献中缺乏综合视角。有必要区分可解释性、可解释性和可理解性等关键概念(这些概念在此背景下经常交替使用),并制定一个全面的框架:为了解 XAI 对 BCI 的需求,我们提出了六个关键研究问题 (RQ),对其目的、应用、可用性和技术可行性进行系统回顾和荟萃分析。我们采用了PRISMA方法--系统综述和荟萃分析的首选报告项目,对2015年及以后发表的研究进行了综述(n=1246)和分析(n=84),以获得关键见解:主要结果:研究结果突出表明,目前的研究主要关注开发人员和研究人员的可解释性,旨在证明结果的合理性并提高模型性能。我们从文献中讨论了 XAI4BCI 的独特方法、优势和局限性。我们从哲学、心理学和社会科学中汲取见解。考虑到在生物识别(BCI)开发和部署生命周期中为不同利益相关者定制可视化和调查预测模型结果的需求不断发展,我们提出了 XAI4BCI 的设计空间:本文是第一篇专门针对 XAI4BCI 研究文章的综述。这篇系统性综述和荟萃分析结果以及所提出的设计空间促使人们就建立 BCI 解释标准、强调当前局限性以及指导 BCI 中 XAI 的未来展开重要讨论。
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