NFBLab—A Versatile Software for Neurofeedback and Brain-Computer Interface Research

Abstract

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.

Decoding Movement From Electrocorticographic Activity: A Review

“…It incorporates several algorithms for processing multichannel recordings. In our laboratory, we have recently developed NFBLab 1 , an open-source software written in Python for implementing a variety of BCI designs (Smetanin et al, 2018b). This software accepts ECoG signals as inputs, as well as EEG and MEG recordings and synchronizes them with motion-tracking information and other multimodal data.…”

Short-Delay Neurofeedback Facilitates Training of the Parietal Alpha Rhythm

“…To monitor the system's total latency in accordance with the recommendations of the CRED-nf checklist [46], we used a direct latency measurement aided with a photosensor attached to the corner of the screen [57]. Figure 3 A explains this method for syncing the "brain time" (top), that is the actual time of the occurrence of neuronal events, and the "PC time", that is the time when these neuronal events are registered by the computer.…”

“…This duration can be decreased by means of optimizing data acquisition and signal processing steps. To this end, we recently developed NFB-Lab software [57], where we implement a novel causal complex-valued finite impulse response (cFIR) approach for simultaneous narrow-band filtering and extracting the instantaneous power of an EEG rhythm [56]. This method allowed us to achieve high accuracy of narrow-band power estimation with the lag as short as 100 ms.…”

Real-Time Decoding of Attentional States Using Closed-Loop EEG Neurofeedback

“…The current available software for real-time EEG neurofeedback (for research) includes BCI2000 1 (Schalk et al, 2004; closed-source C++ software with possible integration with external programs), OpenVIBE 2 (Renard et al, 2010; open-source C++ software with possible integration with external programs), Brainstorm 3 (Tadel, Baillet, Mosher, Pantazis, & Leahy, 2011; open-source Java/Matlab software), BCILAB 4 (Delorme et al, 2011; opensource Matlab toolbox extension for EEGLAB software; Delorme et al, 2011), FieldTrip 5 (Oostenveld, Fries, Maris, & Schoffelen, 2011; open-source Matlab software), and NFBLab 6 (Smetanin, Volkova, Zabodaev, Lebedev, & Ossadtchi, 2018; open-source XML/Python-based software).…”