Non-invasive Brain–Computer Interfaces (BCIs) aim to translate neural activity into external commands, enabling direct communication between the brain and external devices. Despite significant advances, current BCIs still face major limitations in terms of robustness, repeatability, and measurement reliability. In particular, the so-called “BCI inefficiency” (i.e., the inability to detect user intent in up to 30% of subjects) highlights the need for a more rigorous metrological framework for system design, signal acquisition, and performance evaluation.

A metrological approach to BCIs requires the definition of accurate and traceable measurement procedures, along with standardized performance indicators and uncertainty quantification. Beyond signal detection, reproducibility and calibration of measurement chains are fundamental to ensure the comparability of results across users, systems, and laboratories.

This Special Issue aims to gather contributions that address the measurement, validation, and standardization challenges in next-generation BCIs, combining perspectives from neurophysiology, biomedical engineering, and clinical research. Emphasis will be placed on methods and metrics for assessing system performance, inter- and intra-variability and reliability of neurophysiological signals.

Authors are invited to submit original research papers, reviews, and methodological studies dealing with metrological, instrumental, and validation aspects of BCIs and related human–machine interfaces.

Ludovica Gargiulo, Department of Economics, Engineering, and Business Administration, University of Tuscia

Roberto Hornero, Department of Information Engineering, Marche Polytechnic University

Pasquale Arpaia, Università di Napoli Federico II, Italy