Methods in Brain-Computer Interfaces 2026, Volume II

This Research Topic is part of the Methods in Human Neuroscience series.This series aims to highlight the latest experimental techniques and methods used to investigate fundamental questions in Brain-Computer Interfaces (BCI) research. Current areas of rapid progress include non-invasive and invasive neural signal acquisition (EEG, MEG, fNIRS, ECoG, intracortical and emerging endovascular and ultrasound-based recording), deep learning and foundation-model approaches to neural decoding, closed-loop and adaptive systems, and multimodal BCIs that combine neural, physiological, and behavioural signals. Other priorities include speech and motor neuroprostheses, BCI-driven neurorehabilitation and neuromodulation, wearable and home-based systems, and the standardisation, benchmarking, and open sharing of data and code. Review articles or opinions on methodologies or applications, including the advantages and limitations of each, are welcome. This Topic includes technologies and up-to-date methods that help advance science.The contributions to this collection will undergo peer review. Novelty may vary, but the utility of a method or protocol must be evident. We welcome contributions covering all aspects of Brain-Computer Interfaces, from hardware and signal processing to decoding algorithms, user training, validation, and translation outside the laboratory. Submissions will be handled by the team of Topic Editors in the respective sections.Topics of interest include, but are not limited to: • Signal acquisition and sensor technologies, including dry-electrode, wearable, and high-density systems, and hybrid or multimodal recording • Decoding and machine learning methods, including deep learning, transfer learning, domain adaptation, foundation models, and generative AI, with attention to interpretability and robustness • Closed-loop, adaptive, and co-adaptive BCIs, including user-specific calibration and reduced or calibration-free approaches • Communication, motor, and sensory neuroprostheses, and BCI-based neurorehabilitation • BCIs combined with neuromodulation, virtual and augmented reality, or neurofeedback • Validation and reproducibility, including benchmarks, open datasets, shared pipelines, reporting standards, and evaluation in real-world and home settings • Ethical, privacy, and usability considerations, including accessibility, user experience, and responsible use of neural data Topic editors: Ester Martinez-Martin, Davide Borra, Ming Ma, Likun Xia Source: https://www.frontiersin.org/research-topics/87130/methods-in-brain-computer-interfaces-2026-volume-ii