Iester Costanza, Banzhaf Clint, Eldably Ahmed, Schopp Betti, Fallgatter Andreas J, Bonzano Laura, Bove Marco, Ehlis Ann-Christine, Barth Beatrix
University of Genoa, Department of Neuroscience, Rehabilitation, Ophthalmology, Genetics, Maternal and Child Health, Genoa, Italy.
Independent Researcher, Tübingen, Germany.
Neurophotonics. 2025 Apr;12(2):026601. doi: 10.1117/1.NPh.12.2.026601. Epub 2025 May 14.
In recent years, functional near-infrared spectroscopy (fNIRS) has gained increasing attention in the field of neurofeedback. However, there is a lack of freely accessible tools for research in this area that reflect the state of the art in research and technology.
To address this need, we introduce Non-commercial Interface for Neuro-Feedback Acquisitions (NINFA), a user-friendly and flexible freely available neurofeedback application for real-time fNIRS, which is also open to other modalities such as electroencephalography (EEG).
NINFA was developed in MATLAB and the lab streaming layer connection offers maximum flexibility in terms of combination with different fNIRS or EEG acquisition software and hardware.
The user-friendly interface allows measurements without requiring programming expertise. New neurofeedback protocols can be easily created, saved, and retrieved. We provide an example code for real-time data preprocessing and visual feedback; however, users can customize or expand it with appropriate programming skills.
NINFA enables real-time recording, analysis, and feedback of brain signals. We were able to demonstrate the stability and reliability of the computational performance of preprocessing and analysis methods in the current version. NINFA is intended as an application that can, should, and may evolve with the help of contributions from the community.
近年来,功能近红外光谱技术(fNIRS)在神经反馈领域越来越受到关注。然而,在这一领域缺乏反映研究和技术最新水平的可免费获取的研究工具。
为满足这一需求,我们推出了神经反馈采集非商业接口(NINFA),这是一款用户友好且灵活的免费实时fNIRS神经反馈应用程序,它也适用于其他模式,如脑电图(EEG)。
NINFA是在MATLAB中开发的,实验室流层连接在与不同的fNIRS或EEG采集软件和硬件结合方面提供了最大的灵活性。
用户友好的界面允许在无需编程专业知识的情况下进行测量。新的神经反馈协议可以轻松创建、保存和检索。我们提供了一个实时数据预处理和视觉反馈的示例代码;然而,用户可以通过适当的编程技能对其进行定制或扩展。
NINFA能够对脑信号进行实时记录、分析和反馈。我们能够证明当前版本中预处理和分析方法计算性能的稳定性和可靠性。NINFA旨在成为一款能够、应该并可能在社区贡献的帮助下不断发展的应用程序。