Standards Development

Scope

This document defines a framework for fusion readiness and quality profiling of non -invasive brain–computer interface (BCI) data intended for heterogeneous multimodal integration. It specifies principles and requirements for data acquisition, preparation, pre-processing, quality profiling, storage, and verification of multimodal BCI datasets. The framework provides a structured set of processes to support consistent preparation, evaluation, and reuse of BCI data for cross -modal fusion across various application domains.

Purpose

Brain–computer interface (BCI) systems increasingly utilize multimodal datasets that combine neuro -signals (e.g., EEG, fNIRS, and fMRI) with contextual or auxiliary signals in order to improve performance and reliability. However, heterogeneous acquisition environments and device configurations frequently result in inconsistencies in synchronization, signal quality, and metadata representation, limiting interoperability and reproducibility of BCI datasets.

Existing standards address individual elements of BCI data handling, such as biosignal acquisition or general data quality management, but do not provide harmonized requirements for multimodal data fusion readiness or quality profiling. The absence of such guidance leads to inconsistent practices in dataset preparation and evaluation across organizations.

This document establishes a framework for fusion readiness and quality profiling of non -invasive brain– computer interface (BCI) data. The framework defines associated principles and a structured set of processes covering data preparation through to verific ation for heterogeneous multimodal datasets. The framework is intended to enable interoperable, reliable, and reusable BCI data in various domains.

The global market for non-invasive BCI technologies is expanding rapidly across neurotechnology and human–machine interaction applications. Internationally harmonized requirements for multimodal BCI data management are therefore necessary to support intero perability, improve reproducibility, and enhance trust in BCI-based systems.

In addition, datasets prepared in accordance with this framework can provide reliable and structured inputs for artificial intelligence training, analytics, and machine learning applications involving neuro -signal data. By improving dataset integrity, synchronization consistency, and traceability, the framework supports the development of trustworthy AI models and data-driven neurotechnology applications.