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GB/T 47127-2026: Information technology - Brain-computer interfaces - Multi-modal data format
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Information technology - Brain-computer interfaces - Multi-modal data format
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Standard similar to GB/T 47127-2026 GB/T 44368 | GB/T 45181 | GB/T 41239 | GB/T 20533 | GB/T 47118 | Basic data | Standard ID | GB/T 47127-2026 (GB/T47127-2026) | | Description (Translated English) | Information technology - Brain-computer interfaces - Multi-modal data format | | Sector / Industry | National Standard (Recommended) | | Classification of Chinese Standard | L67 | | Classification of International Standard | 35.020 | | Date of Issue | 2026-01-28 | | Date of Implementation | 2026-08-01 | GB/T 47127-2026: Information technology - Brain-computer interfaces - Multi-modal data format---This is a DRAFT version for illustration, not a final translation. Full copy of true-PDF in English version (including equations, symbols, images, flow-chart, tables, and figures etc.) will be manually/carefully translated upon your order.
ICS 35.020
CCSL67
National Standards of the People's Republic of China
Information technology brain-computer interface multimodal data format
Published on 2026-01-28
Implemented on August 1, 2026
State Administration for Market Regulation
The State Administration for Standardization issued a statement.
Table of contents
Preface III
Introduction IV
1.Scope 1
2 Normative References 1
3.Terms and Definitions 1
4.Abbreviations 2
5.Overall Structure 3
6.Directory Structure 3
6.1 Overview 3
6.2 Directory Naming Rules 4
7 Data Files 4
7.1 Overview 4
7.2 Dataset Metadata File 4
7.3 Subject Metadata Files 5
7.4 Session Metadata File 5
7.5 Signal Recording Data Files and Modal Metadata Files 6
7.6 Event Metadata File 14
References 16
Foreword
This document complies with the provisions of GB/T 1.1-2020 "Standardization Work Guidelines Part 1.Structure and Drafting Rules of Standardization Documents".
Drafting.
Please note that some content in this document may involve patents. The issuing organization of this document assumes no responsibility for identifying patents.
This document was proposed and is under the jurisdiction of the National Information Technology Standardization Technical Committee (SAC/TC28).
This document was drafted by. Zhejiang University, China Electronics Technology Standardization Institute, Nanhu Brain-Computer Interdisciplinary Research Institute, and Hangzhou Dianzi University.
University of Science and Technology of China, South China Normal University, Tianjin University, University of Science and Technology of China, Beijing University of Posts and Telecommunications, Beijing Mechanical Equipment Research Institute, Artificial Intelligence and Digital Technology
Guangdong Provincial Laboratory of Economics (Guangzhou), Hangzhou Qingshi Yongjun Medical Equipment Co., Ltd., Hangzhou Nuoxinjia Electronic Technology Co., Ltd., Electronic Technology
Universities, Shanghai University, China Academy of Information and Communications Technology, China Mobile (Hangzhou) Information Technology Co., Ltd., Beijing Chipwise Neurotechnology Co., Ltd.
Zhejiang Qiangnao Technology Co., Ltd., Institute of Biomedical Engineering, Chinese Academy of Medical Sciences, Zhijiang Laboratory, and Boruikang Technology (Shanghai) Co., Ltd.
Limited Company, Shanghai Jiao Tong University, Zhejiang Nurotron Neuroelectronics Technology Co., Ltd., Hangzhou Rongnao Technology Co., Ltd., Harbin Engineering University
University.
The main drafters of this document are. Pan Gang, Li Nan, Liu Xiangang, Fan Kefeng, Dong Jian, Yu Yuntao, Wang Yueming, Zhang Shaomin, Kong Wanzeng, and Pan Jiahui.
Ming Dong, Chen Xun, Yang Chen, Zhang Lijian, Li Yuanqing, Li Xiang, Sun Yu, Yao Lin, Sun Wenjing, Cui Bing, Zhang Jianhai, Yang Yuxiao, Hao Yaoyao, Chang Yuan, Xiao Jun
Zhao Sha, Qi Yu, Zheng Nenggan, Xiao Xiaolin, Liu Aiping, Zhu Jiang, Qin Yun, Yang Banghua, Liang Liyan, Wang Liuqing, Xie Jue, Adis, Pu Jiangbo, Huang Dandan
Xu Honglai, Wu Ruijia, Huang Sui, Dong Shurong, Xu Li.
Introduction
Brain-computer interfaces (BCIs) are a rapidly developing cutting-edge interdisciplinary technology that has gained traction in recent years, particularly in areas such as neurorehabilitation and human-machine interaction.
It shows broad application prospects in multiple fields such as interaction and intelligent control. Various brain signal acquisition technologies, such as electroencephalography (EEG) and magnetoencephalography (MEG), are also being utilized.
(MEG), functional near-infrared spectroscopy (fNIRS), functional magnetic resonance imaging (fMRI), intracranial electroencephalography (iEEG), and microelectrode electrophysiology
(uEphys) provides crucial support for a deeper understanding of brain activity and forms an important foundation for brain-computer interface systems.
However, current brain signal acquisition technologies lack unified standards in data structure, metadata description, and file organization, leading to data...
Due to structural heterogeneity, poor interoperability, and high reuse costs, brain-computer interface technology is not conducive to cross-system and cross-institutional data sharing and collaboration, and also limits its development.
The large-scale development of technology.
This document aims to establish a unified and scalable data format specification applicable to brain-computer interfaces from different brain signal acquisition modalities.
This data format represents, stores, and manages data and its related metadata. It supports the recording of unimodal data as well as multimodal synchronous acquisition.
Scenario. By defining a consistent file structure, naming conventions, and metadata fields, this file will help improve data quality and enhance compatibility.
It also promotes collaboration among research institutions, system developers, and data users.
Furthermore, the data format specified in this document adopts a modular and scalable framework design, supporting future data acquisition from emerging brain-computer interface technologies.
The expansion and supplementation of techniques and data types can continuously adapt to the development needs of the brain-computer interface field through supplementary documentation or content revisions.
Information technology brain-computer interface multimodal data format
1 Scope
This document establishes the overall structure of the brain-computer interface multimodal data format, and specifies the directory hierarchy and different types of data files.
Content and naming conventions.
This document applies to the storage of brain-computer interface data acquired simultaneously in a single or multimodal manner, covering modalities including electroencephalography (EEG) and functional neurological markers.
Near-infrared spectroscopy, magnetoencephalography, functional magnetic resonance imaging, intracranial electroencephalography, and microelectrode electrophysiology.
2 Normative references
This document has no normative references.
3 Terms and Definitions
The following terms and definitions apply to this document.
3.1
A system for establishing a direct communication connection between the central nervous system of humans or other animals and external hardware and software systems.
Note. Brain-computer interfaces enable one-way or two-way communication with external devices and can be used to achieve control and/or feedback.
3.2
Data format
A standardized description of the logical structure and physical representation of data.
3.3
modality
Different forms or channels used to acquire, express, or perceive information.
Note. In the field of brain-computer interfaces, modality usually refers to the source or measurement method of the signal.
3.4
A graphical record of the voltage change over time obtained using scalp electrodes placed at designated locations.
[Source. IEC 60050-891.1998[1], 891-04-23]
3.5
Magnetic resonance imaging (MRI) is a technique used to record blood flow to functional areas of the brain.
Note. This technology relies on cerebral blood flow and neuronal activation coupling.
3.6
Optical brain monitoring technology that uses near-infrared light for functional neuroimaging.
Note. Functional near-infrared spectroscopy uses near-infrared light to measure brain activity to estimate cortical hemodynamic activity, which is a measure of neural activity.
response.
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