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.
Tendon-driven Flexible Continuum Manipulators (FCMs) have proven their potential usability in confined and unstructured environments where rigid-link manipulators under-perform. However, inverse kinematics remains a challenge. This paper presents a new approach that relates the manipulator's workspace to the actuation space. In this approach, a virtual actuation space is defined, comprising a virtual tendon and the bending plane of the manipulator. The change in lengths of the actuating tendons that cause the bending is mapped to the change in the length of a single virtual tendon. An example of a tip-actuated three-tendon FCM provides a detailed description of the forward and inverse kinematics and experimental validations. The experimental results show a reasonable match between the commands and estimates. This approach reduces the order of the actuation space from three tendons to one tendon for the example system, thus, providing a simpler method for designing and controlling FCMs with multiple sections.