Smetanin Nikolai, Volkova Ksenia, Zabodaev Stanislav, Lebedev Mikhail A, Ossadtchi Alexei
Center for Bioelectric Interfaces, National Research University Higher School of Economics, Moscow, Russia.
Medical Computer Systems, Zelenograd, Russia.
Front Neuroinform. 2018 Dec 24;12:100. doi: 10.3389/fninf.2018.00100. eCollection 2018.
Neurofeedback (NFB) is a real-time paradigm, where subjects learn to volitionally modulate their own brain activity recorded with electroencephalographic (EEG), magnetoencephalographic (MEG) or other functional brain imaging techniques and presented to them via one of sensory modalities: visual, auditory or tactile. NFB has been proposed as an approach to treat neurological conditions and augment brain functions. Although the early NFB studies date back nearly six decades ago, there is still much debate regarding the efficiency of this approach and the ways it should be implemented. Partly, the existing controversy is due to suboptimal conditions under which the NFB training is undertaken. Therefore, new experimental tools attempting to provide optimal or close to optimal training conditions are needed to further exploration of NFB paradigms and comparison of their effects across subjects and training days. To this end, we have developed open-source NFBLab, a versatile, Python-based software for conducting NFB experiments with completely reproducible paradigms and low-latency feedback presentation. Complex experimental protocols can be configured using the GUI and saved in NFBLab's internal XML-based language that describes signal processing tracts, experimental blocks and sequences including randomization of experimental blocks. NFBLab implements interactive modules that enable individualized EEG/MEG signal processing tracts specification using spatial and temporal filters for feature selection and artifacts removal. NFBLab supports direct interfacing to MNE-Python software to facilitate source-space NFB based on individual head models and properly tailored individual inverse solvers. In addition to the standard algorithms for extraction of brain rhythms dynamics from EEG and MEG data, NFBLab implements several novel in-house signal processing algorithms that afford significant reduction in latency of feedback presentation and may potentially improve training effects. The software also supports several standard BCI paradigms. To interface with external data acquisition devices NFBLab employs Lab Streaming Layer protocol supported by the majority of EEG vendors. MEG devices are interfaced through the Fieldtrip buffer.
神经反馈（NFB）是一种实时范式，受试者通过这种范式学会自主调节用脑电图（EEG）、脑磁图（MEG）或其他功能性脑成像技术记录的自身脑活动，并通过视觉、听觉或触觉等感觉模态之一呈现给他们。NFB已被提议作为一种治疗神经疾病和增强脑功能的方法。尽管早期的NFB研究可以追溯到近六十年前，但关于这种方法的效率以及实施方式仍存在很多争议。部分原因是进行NFB训练的条件不理想。因此，需要新的实验工具来提供最佳或接近最佳的训练条件，以进一步探索NFB范式并比较它们在不同受试者和训练日的效果。为此，我们开发了开源的NFBLab，这是一款基于Python的通用软件，用于进行具有完全可重复范式和低延迟反馈呈现的NFB实验。复杂的实验协议可以使用图形用户界面（GUI）进行配置，并以NFBLab基于XML的内部语言保存，该语言描述了信号处理路径、实验块和序列，包括实验块的随机化。NFBLab实现了交互式模块，可使用空间和时间滤波器进行特征选择和伪迹去除，从而实现个性化的EEG/MEG信号处理路径指定。NFBLab支持直接与MNE-Python软件接口，以便基于个体头部模型和适当定制的个体逆解算器促进源空间NFB。除了从EEG和MEG数据中提取脑节律动态的标准算法外，NFBLab还实现了几种新颖的内部信号处理算法，可显著减少反馈呈现的延迟，并可能潜在地提高训练效果。该软件还支持几种标准的脑机接口（BCI）范式。为了与外部数据采集设备接口，NFBLab采用了大多数EEG供应商支持的实验室流层（Lab Streaming Layer）协议。MEG设备通过Fieldtrip缓冲区进行接口。
