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Updated: Jun 20, 2025

Assessment and Communication for People with Disorders of Consciousness

Assessment and Communication for People with Disorders of Consciousness

Published on: August 1, 2017

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

Param Rajpura¹, Hubert Cecotti², Yogesh Kumar Meena¹

¹Human-AI Interaction (HAIx) Lab, Indian Institute of Technology Gandhinagar, Gandhinagar, India.

Journal of Neural Engineering

|July 19, 2024

Summary

This summary is machine-generated.

Explainable Artificial Intelligence (XAI) in Brain-Computer Interfaces (BCI) is crucial for trust, but current research lacks integration. This review proposes a framework to guide future XAI for BCI development and standards.

Keywords:

brain-machine (computer) interface explainable artificial intelligence interpretable machine learning numerical AI symbolic AI

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Area of Science:

Neuroscience and Artificial Intelligence
Human-Computer Interaction
Cognitive Science

Background:

Brain-Computer Interfaces (BCIs) utilize predictive models for high-stakes applications, interpreting complex brain signals.
Explainability in BCI models is challenging, often trading accuracy for transparency, hindering trust.
Existing literature lacks an integrated perspective on Explainable Artificial Intelligence for BCI (XAI4BCI), conflating key concepts like explainability, interpretability, and understanding.

Purpose of the Study:

To provide an integrated perspective on XAI techniques applied to BCIs.
To differentiate key concepts and formulate a comprehensive framework for XAI4BCI.
To address the need for explainability across various stakeholders in BCI development and deployment.

Main Methods:

Systematic review and meta-analysis of 1246 studies published from 2015 onwards, guided by PRISMA methodology.
Analysis of 84 selected studies to extract key insights on XAI4BCI.
Formulation of six key research questions covering purposes, applications, usability, and technical feasibility of XAI in BCI.

Main Results:

Current research predominantly focuses on interpretability for developers and researchers to justify outcomes and improve model performance.
Unique approaches, advantages, and limitations of XAI4BCI were identified and discussed.
A design space for XAI4BCI was proposed, emphasizing visualization and stakeholder-customized outcomes.

Conclusions:

This paper is the first to exclusively review XAI4BCI research, offering a novel integrated perspective.
Findings highlight the need for establishing standards for BCI explanations and address current limitations.
The proposed framework and design space will guide future research and development in XAI for BCI.