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

MEG signatures of a perceived match or mismatch between individual and group opinions

Humans often adjust their opinions to the perceived opinions of others. Neural responses to a perceived match or mismatch between individual and group opinions have been investigated previously, but some findings are inconsistent. In this study, we used magnetoencephalographic source imaging to investigate further neural responses to the perceived opinions of others. We found that group opinions mismatching with individual opinions evoked responses in the anterior and posterior medial prefrontal cortices, as well as in the temporoparietal junction and ventromedial prefrontal cortex in the 220-320 and 380-530 ms time windows. Evoked responses were accompanied by an increase in the power of theta oscillations (4-8 Hz) over a number of frontal cortical sites. Group opinions matching with individual opinions evoked an increase in amplitude of beta oscillations (13-30 Hz) in the anterior cingulate and ventral medial prefrontal cortices. Based on these results, we argue that distinct valuation and performance-monitoring neural circuits in the medial cortices of the brain may monitor compliance of individual behavior to the perceived group norms.Peer reviewe

Navigation Patterns and Scent Marking: Underappreciated Contributors to Hippocampal and Entorhinal Spatial Representations?

According to the currently prevailing theory, hippocampal formation constructs and maintains cognitive spatial maps. Most of the experimental evidence for this theory comes from the studies on navigation in laboratory rats and mice, typically male animals. While these animals exhibit a rich repertoire of behaviors associated with navigation, including locomotion, head movements, whisking, sniffing, raring and scent marking, the contribution of these behavioral patterns to the hippocampal spatially-selective activity has not been sufficiently studied. Instead, many publications have considered animal position in space as the major variable that affects the firing of hippocampal place cells and entorhinal grid cells. Here we argue that future work should focus on a more detailed examination of different behaviors exhibited during navigation to better understand the mechanism of spatial tuning in hippocampal neurons. As an inquiry in this direction, we have analyzed data from two datasets, shared online, containing recordings from rats navigating in square and round arenas. Our analyses revealed patchy navigation patterns, evident from the spatial maps of animal position, velocity and acceleration. Moreover, grid cells available in the datasets exhibited similar periodicity as the navigation parameters. These findings indicate that activity of grid cells could affect navigation parameters and/or vice versa. Additionally, we speculate that scent marks left by navigating animals could contribute to neuronal responses while rats and mice sniff their environment; the act of sniffing could modulate neuronal discharges even in virtual visual environments. Accordingly, we propose that future experiments should contain additional controls for navigation patterns, whisking, sniffing and maps composed of scent marks

MEG dataset for the article "MEG signature of social conformity: Evidence from evoked and induced responses"

<p>The data set contains averaged MEG measurements of the event-related fields and 3d reconstructions of individual cortical surfaces. Data analysis was performed with Brainstorm (Tadel et al. 2011), which is documented and freely available for download online under the GNU general public license (http://neuroimage.usc.edu/brainstorm). The data is organized in brainstorm database format and can be imported directly using Brainstorm software. The participant's identities are encoded, individual facial features are blurred and  any other personal information is also removed from the data. </p