  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.
