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

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

Consensus on the reporting and experimental design of clinical and cognitive-behavioural neurofeedback studies (CRED-nf checklist)

Neurofeedback has begun to attract the attention and scrutiny of the scientific and medical mainstream. Here, neurofeedback researchers present a consensus-derived checklist that aims to improve the reporting and experimental design standards in the field.</p

PSIICOS projection optimality for EEG and MEG based functional coupling detection

Functional connectivity is crucial for cognitive processes in the healthy brain and serves as a marker for a range of neuropathological conditions. Non-invasive exploration of functional coupling using temporally resolved techniques such as MEG allows for a unique opportunity of exploring this fundamental brain mechanism.The indirect nature of MEG measurements complicates the estimation of functional coupling due to the volume conduction and spatial leakage effects. In the previous work (Ossadtchi et al., 2018), we introduced PSIICOS, a method that for the first time allowed us to suppress the volume conduction effect and yet retain information about functional networks whose nodes are coupled with close to zero or zero mutual phase lag.In this paper, we demonstrate analytically that the PSIICOS projection is optimal in achieving a controllable trade-off between suppressing mutual spatial leakage and retaining information about zero- or close to zero-phase coupled networks. We also derive an alternative solution using the regularization-based inverse of the mutual spatial leakage matrix and show its equivalence to the original PSIICOS.We then discuss how PSIICOS solution to the functional connectivity estimation problem can be incorporated into the conventional source estimation framework. Instead of sources, the unknowns are the elementary dyadic networks and their activation time series are formalized by the corresponding source-space cross-spectral coefficients. This view on connectivity estimation as a regression problem opens up new opportunities for formulating a set of principled estimators based on the rich intuition accumulated in the neuroimaging community

Synthetic and real EEG datasets for closed-loop neuroscience

The dataset is made primarily for the task of real-time low latency filtering of the EEG data in the closed loop neuroscience experiments and for EEG forecasting task. The dataset consists of a real data and 5 options of the synthetic data of varying difficulty. The real dataset consists of 25 people involved into the P4 alpha neurofeedback training. Its total size is about 16.3 hours. A more detailed instruction for this file is provided in the file Real dataset instructions.txt. Synthetic data is generated in 5 different ways: sine wave with white noise, sine wave with pink noise, narrow-band filtered pink noise sample with pink noise, state-space model with white noise and state-space model with pink noise. Each of these datasets has about 34.5 hours of data. It is generated similarly to (Wodeyar et al, 2021). A more detailed instruction for the synthetic dataset can be found in the file Synthetic datasets instructions.txt. In LowLatencyEEGFiltering.zip one can find a code for the models used in our paper for low-latency filtering with this data. If you use our data or code please cite: https://www.doi.org/10.1088/1741-2552/acf7f