Search

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 in a reasonably ecological setting.The indirect nature of MEG measurements complicates the the estimation of functional coupling due to the spatial leakage effects. In previous work (Ossadtchi et al., 2018), we introduced PSIICOS, a method that for the first time allowed us to suppress the spatial leakage 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-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. This approach allows us to incorporate the PSIICOS solution into the conventional source estimation framework. Instead of sources, the unknowns are the elementary networks and their activation timeseries are formalized by the corresponding source-space cross-spectral coefficients.Additionally, we outline potential avenues for future research to enhance functional coupling estimation and discuss alternative estimators that parallel the established source estimation approaches. Finally, we propose that the PSIICOS framework is well-suited for Bayesian techniques and offers a principled way to incorporate priors derived from structural connectivity.

Magnetic sensors developed for application in magnetoencephalography must meet a number of requirements; the main ones are compactness, sensitivity and response speed. We present a quantum optically pumped atomic sensor with cell volume of 0.5 cm3 that meets these requirements and is operable in nonzero magnetic fields. The ultimate sensitivity of the sensor was estimated as (using the criteria of the ratio of the slope of the magnetic resonance signal to the shot noise spectral density) to be better than 5 fT/ $\sqrt {\mathstrut }$ Hz. The actual sensitivity, measured in a gradiometric scheme, reaches 13 fT/ $\sqrt {\mathstrut }$ Hz per sensor. We also present a novel and fast algorithm for optimization of the geometric properties of non-zero field sensor array with respect to maximization of the information transfer rate for cortical sources.

Objective. Speech decoding, one of the most intriguing brain-computer interface applications, opens up plentiful opportunities from rehabilitation of patients to direct and seamless communication between human species. Typical solutions rely on invasive recordings with a large number of distributed electrodes implanted through craniotomy. Here we explored the possibility of creating speech prosthesis in a minimally invasive setting with a small number of spatially segregated intracranial electrodes. Approach. We collected one hour of data (from two sessions) in two patients implanted with invasive electrodes. We then used only the contacts that pertained to a single stereotactic electroencephalographic (sEEG) shaft or an electrocorticographic (ECoG) stripe to decode neural activity into 26 words and one silence class. We employed a compact convolutional network-based architecture whose spatial and temporal filter weights allow for a physiologically plausible interpretation. Main results. We achieved on average 55% accuracy using only six channels of data recorded with a single minimally invasive sEEG electrode in the first patient and 70% accuracy using only eight channels of data recorded for a single ECoG strip in the second patient in classifying 26+1 overtly pronounced words. Our compact architecture did not require the use of pre-engineered features, learned fast and resulted in a stable, interpretable and physiologically meaningful decision rule successfully operating over a contiguous dataset collected during a different time interval than that used for training. Spatial characteristics of the pivotal neuronal populations corroborate with active and passive speech mapping results and exhibit the inverse space-frequency relationship characteristic of neural activity. Compared to other architectures our compact solution performed on par or better than those recently featured in neural speech decoding literature. Significance. We showcase the possibility of building a speech prosthesis with a small number of electrodes and based on a compact feature engineering free decoder derived from a small amount of training data.

Cross-frequency coupling (CFC) is typical for the operation of neural networks from different areas of the brain. This, for example, can be characterized by “рacemaker” neurons activity, the structure of these part’s crust, etc. Thus, the highest interest is not the correlation of those areas, but the synchronous activity of the areas in time at different frequencies. Event-related events can induce the work of neurons, but each at its own frequency. It looks like a synchronous manifestation of activity at different points in time, with a lag, but appearing at different frequencies. Correlation methods and coherence for CFC detection are not suitable, since they are for monofrequencies, and a long time series is required. The connectedness of neurons ensemble’s work in time is effectively considered by methods such as bispectrum and CTDC. In this work, we compared these two methods as well as their hybrid. According to the results, the CTDC method proved to be more accurate, both in spatial localization and in inter-frequency.

The access to activity of subcortical structures offers unique opportunity for building intention dependent brain-computer interfaces, renders abundant options for exploring a broad range of cognitive phenomena in the realm of affective neuroscience including complex decision making processes and the eternal free-will dilemma and facilitates diagnostics of a range of neurological deceases. So far this was possible only using bulky, expensive and immobile fMRI equipment. Here we present an interpretable domain grounded solution to recover the activity of several subcortical regions from the multichannel EEG data and demonstrate up to 60% correlation between the actual subcortical blood oxygenation level dependent sBOLD signal and its EEG-derived twin. Then, using the novel and theoretically justified weight interpretation methodology we recover individual spatial and time-frequency patterns of scalp EEG predictive of the hemodynamic signal in the subcortical nuclei. The described results not only pave the road towards wearable subcortical activity scanners but also showcase an automatic knowledge discovery process facilitated by deep learning technology in combination with an interpretable domain constrained architecture and the appropriate downstream task., Comment: 11 pages. Add acknowledgment

Brain-Computer Interface (BCI) is a system enabling direct communication between the brain and external devices. It has large potential in neurorehabilitation. New optically-pumped magnetometer (OPM)-based recording systems have large potential among noninvasive electroencephalographic and traditional magnetoencephalographic BCI-systems., that should be revealed., however. Here we demonstrated good performance of the hand-movement imagery BCI., built on low number of OPM-sensors.

The use of Neurofeedback (NFB) as a therapeutic tool remains controversial. Its efficacy is subject to many factors including subject's motivation, content of the instruction, resting-state amplitude before the training and subject's ability to concentrate on the task. Here we introduce a novel proactive neurofeedback paradigm and explore its use for training t 0 increase parietal alpha rhythm power in a visually rich gamified environment. Hybrid control (a keyboard with alpha rhythm), mixed feedback delivery (discrete and continuous), gamified procedure and decreased feedback delay are supposed to engage the subject and positively influence the training outcome. Results show a gradually ascending learning curve and significant differences between baselines in the real feedback group compared to a control group with fake feedback after only 30 minutes of the entertainful training. Overall, as demonstrated by our pilot study with 20 participants, the novel proactive paradigm appeared to be an efficient and engaging tool for conditioning parietal alpha power. The subjects from the experimental group show significant growth in the incidence rate of alpha bursts over the course of training as well as exhibit sustainable changes in the post-training interval. Such a gamified way for conditioning brain activity could pave a road for the future non-pharmacological treatment of a broad range of neurodegenerative disorders and achievement of peak cognitive performance.

We present a framework that allows to investigate the fine-grained patterns of interictal spikes present in magne-toencephalographic (MEG) data of epileptic patients. This analysis could provide a valuable information regarding epileptogenic zone (EZ) location. We use a traveling wave model to regularize the MEG inverse problem. Our algorithm represents spike propagation patterns as a superposition of local waves traveling along radial paths stemming from a single origin. Using the positively constrained LASSO technique we scan over the wave onset moment and propagation velocity parameters to determine their combination that yields the best fit to the MEG sensor data of each spike. We use realistically simulated MEG data to validate the algorithm ability to successfully track interictal activity on a millimeter-millisecond scale. Also we examine MEG data from three patients with drug-resistant epilepsy. Wave-like spike patterns with clear propagation dynamics are found in a fraction of spikes, whereas the other fraction can not be explained by the wave propagation model with a small number of propagation directions. Moreover, the spike waves with clear propagation dynamics exhibit spatial segregation and match the clinical records on EZ availabel for two patients out of three.

Background: Brain-computer interfaces (BCIs) decode neural activity and extract from it information that can be meaningfully interpreted. One of the most intriguing opportunities is to employ BCIs for decoding speech, a uniquely human trait, which opens up plentiful applications from rehabilitation of patients to a direct and seamless communication between human species. To decipher neuronal code complex deep neural networks furnish only limited success. In such solutions an iffy performance gain is achieved with uniterpretable decision rules characterised by thousands of parameters to be identified from a limited amount of training data. Our recent experience shows that when applied to neural activity data compact neural networks with trainable and physiologically meaningful feature extraction layers [1] deliver comparable performance, ensure robustness of the learned decision rules and offer the exciting opportunity of automatic knowledge discovery. Methods: We collected approximately one hour of data (from two sessions) where we recorded stereotactic EEG (sEEG) activity during overt speech (6 different randomly shuffled phrases and rest). We have also recorded synchronized audio speech signal. The sEEG recording was carried out in an epilepsy patient implanted for medical reasons with an sEEG electrode passing through Broca area with 6 contacts spaced at 5 mm. We then used a compact convolutional network-based architecture to recover speech mel-cepstrum coefficients followed by a 2D convolutional network to classify individual words. We then interpreted the former network weights using the theoretically justified approach devised by us earlier [1]. Results: We achieved on average 44% accuracy in classifying 26+ 1 words (3.7% chance level) using only 6 channels of data recorded with a single minimally invasive sEEG electrode. We compared the performance of our compact convolutional network to that of the DenseNet-like architecture that has recently been featured in neural speech decoding literature and did not find statistically significant performance differences. Moreover, our architecture appeared to be able to learn faster and resulted in a stable, interpretable and physiologically meaningful decision rule successfully operating over a contiguous data segment no-overlapping with the training data interval. Spatial characteristics of neuronal population pivotal to the task corroborate the results of active speech mapping procedure and frequency domain patterns show primary involvement of the high frequency activity. Conclusions: Most of the speech decoding solutions availabel to date either use potentially harmful intracortical electrodes or rely on the data recorded with impractically massive multielectrode grids covering large cortical area. Here we for the first time achieved practically usable decoding accuracy for the vocabulary of 26 words + 1 silence class backed by only 6 channels of cortical activity sampled with a single sEEG shaft. The decoding was implemented using a compact and interpretable architecture which ensures robustness of the solution and requires small amount of training data. The proposed approach is the first step towards minimally invasive implantable BCI solution for restoring speech function.

Subjective time perception is a complex phenomenon that normally involves interaction of different sensory modalities, as well as cognitive factors [15]. Music perception is another fascinating process that involves hierarchically distributed brain circuitry, implementing prediction-based coding [7] in a highly context-dependent way. We hypothesize that familiarity of a music piece interacts with the brain's predictive mechanisms [6] and therefore may influence subjective time perception. In this context, if the perceived time duration reflects brain's effort spent for a tune's progression prediction, then familiar tunes should reduce the subjectively perceived time. At the same time, if a memory trace left by the tune affects the temporal judgement, the familiar and the most recent tune should be perceived as longer when compared to the unfamiliar and not so recently presented one.

Meditation is a special state of consciousness associated with distinct physiological and brain correlates. Multiple investigations of these correlates reported controversial results and do not lead to full understanding of the underlying processes. However., most studies have proven beneficial effects of regular meditation. Here we investigated the dynamics of multiple neurophysiological parameters: respiration., photoplethysmography., electroencephalography (EEG)., during a controlled staged meditation session. We measured the physiological and brain changes at rest and during guided Taoist meditation in both experienced meditators (N=13) and subjects without any meditation experience (N=15). All subjects followed the same set of audio instructions. We assessed brain activity and autonomic functions at different meditation stages. Indicators of autonomic nervous system activity that differed significantly between the experienced meditators and the novices were considered as indices of interest. A representational similarity analysis was employed to analyze the coupling of these autonomic-function markers with the EEG features. Consistent with previous studies., the experienced meditators demonstrated higher levels of autonomic and central nervous system interaction in the meditative state compared with novice subjects.

The final aim of our research is to develop a braincomputer interface (BCI) for olfaction. Our research program relies on modern olfactory displays and advanced processing of electroencephalography (EEG) and respiratory data in order to develop methods for robust olfactory BCI systems. Here we present the initial results from 17 subjects of our ongoing study. Applying k-nearest neighbors algorithm (k-NN) classification methods we achieve up to 79.9% accuracy for within subject classification of EEG signals for odor pairs. We propose some methods for further improvement of classification algorithm. EEG classification for different olfactory stimuli is the first step in this research, followed by the development of odor-imagery BCIs and odor-based neurofeedback.

This study aims to identify correlations between the directions of the hand movements and the brain signals recorded from the brain surface using electrocorticography (ECoG). We suppose that the coding of the hand movement direction occurs in the motor cortex and could be detected with ECoG. Using representational similarity analysis (RSA) and the cosine tuning assumption we confirmed that indeed, there is a significant concordance between the brain signals and the hand movement directions. And the main areas of the brain responsible for this have been identified.

Meditation is a consciousness state associated with specific physiological and neural correlates. Numerous investigations of these correlates reported controversial results which prevented a consistent depiction of the underlying neurophysiological processes. Here we investigated the dynamics of multiple neurophysiological indicators during a staged meditation session. We measured the physiological changes at rest and during the guided Taoist meditation in experienced meditators and naive subjects. We recorded EEG, respiration, galvanic skin response, and photoplethysmography. All subjects followed the same instructions split into 16 stages. In the experienced meditators group we identified two subgroups with different physiological markers dynamics. One subgroup showed several signs of general relaxation evident from the changes in heart rate variability, respiratory rate, and EEG rhythmic activity. The other subgroup exhibited mind concentration patterns primarily noticeable in the EEG recordings while no autonomic responses occurred. The duration and type of previous meditation experience or any baseline indicators we measured did not explain the segregation of the meditators into these two groups. These results suggest that two distinct meditation strategies could be used by experienced meditators, which partly explains the inconsistent results reported in the earlier studies evaluating meditation effects. Our findings are also relevant to the development of the high-end biofeedback systems.

In the current article we present a first solid-state sensor feasible for magnetoencephalography (MEG), and working at room temperature. The sensor is a fluxgate magnetometer based on yttrium-iron garnet films (YIGM). In this feasibility study, we prove the concept of using the YIGM in terms of MEG by registering a simple brain-induced field: the human alpha rhythm (The data that support the findings of this study are available from the corresponding author upon reasonable request). All the experiments and results are validated with the usage of another kind of high-sensitive magnetometers - optically pumped magnetometer (OPM), which currently appears to be well-established in terms of MEG.

People often change their beliefs by succumbing to an opinion of others. Such changes are often referred to as effects of social influence. While some previous studies have focused on the reinforcement learning mechanisms of social influence or on its internalization, others have reported evidence of changes in sensory processing evoked by social influence of peer groups. In this study, we used magnetoencephalographic (MEG) source imaging to further investigate the long-term effects of agreement and disagreement with the peer group. The study was composed of two sessions. During the first session, participants rated the trustworthiness of faces and subsequently learned group rating of each face. In the first session, a neural marker of an immediate mismatch between individual and group opinions was found in the posterior cingulate cortex, an area involved in conflict-monitoring and reinforcement learning. To identify the neural correlates of the long-lasting effect of the group opinion, we analysed MEG activity while participants rated faces during the second session. We found MEG traces of past disagreement or agreement with the peers at the parietal cortices 230 ms after the face onset. The neural activity of the superior parietal lobule, intraparietal sulcus, and precuneus was significantly stronger when the participant's rating had previously differed from the ratings of the peers. The early MEG correlates of disagreement with the majority were followed by activity in the orbitofrontal cortex 320 ms after the face onset. Altogether, the results reveal the temporal dynamics of the neural mechanism of long-term effects of disagreement with the peer group: early signatures of modified face processing were followed by later markers of long-term social influence on the valuation process at the ventromedial prefrontal cortex.

Magnetic sensors developed for application in magnetoencephalography must meet a number of requirements; the main ones are compactness, sensitivity and response speed. We present a quantum optically pumped atomic sensor with cell volume of 0.5cm3 that meets these requirements and is operable in nonzero magnetic fields. The ultimate sensitivity of the sensor was estimated as (using the criteria of the ratio of the slope of the magnetic resonance signal to the shot noise spectral density) to be better than 5 fT/Hz1/2. The actual sensitivity, measured in a gradiometric scheme, reaches 13 fT/Hz1/2 per sensor. We also present a novel and fast algorithm for optimization of the geometric properties of non-zero field sensor array with respect to maximization of the information transfer rate for cortical sources.

People often change their beliefs by succumbing to an opinion of others. Such changes are often referred to as effects of social influence. While some previous studies have focused on the reinforcement learning mechanisms of social influence or on its internalization, others have reported evidence of changes in sensory processing evoked by social influence of peer groups. In this study, we used magnetoencephalographic (MEG) source imaging to further investigate the long-term effects of agreement and disagreement with the peer group. The study was composed of two sessions. During the first session, participants rated the trustworthiness of faces and subsequently learned group rating of each face. In the first session, a neural marker of an immediate mismatch between individual and group opinions was found in the posterior cingulate cortex, an area involved in conflict-monitoring and reinforcement learning. To identify the neural correlates of the long-lasting effect of the group opinion, we analysed MEG activity while participants rated faces during the second session. We found MEG traces of past disagreement or agreement with the peers at the parietal cortices 230 ms after the face onset. The neural activity of the superior parietal lobule, intraparietal sulcus, and precuneus was significantly stronger when the participant’s rating had previously differed from the ratings of the peers. The early MEG correlates of disagreement with the majority were followed by activity in the orbitofrontal cortex 320 ms after the face onset. Altogether, the results reveal the temporal dynamics of the neural mechanism of long-term effects of disagreement with the peer group: early signatures of modified face processing were followed by later markers of long-term social influence on the valuation process at the ventromedial prefrontal cortex.

One of the main methodological problems in evaluation of functional connectivity is the spatial leakage (SL) effect which occurs due to volume conduction and leads to false positives in coherence or phase-locking estimates. Several solutions have been already suggested, including the use of the imaginary part of coherency or cross-spectrum. Because these standard metrics are insensitive to zero-phase interactions, they prevent detection of false coupling, resulting from SL, but may underestimate true physiological interactions, characterized by close-to-zero phase lags. Due to the broad neurophysiological evidence, such interactions should not be excluded from consideration. The recently proposed method, referred as Phase Shift Invariant Imaging of Coherent Sources (PSIICOS), became the first implementation of the algorithm which reliably detects interactions for all the range of phase-lags by suppressing the power of SL subspace components of cross-spectrum. However, connectivity values obtained via PSIICOS are non-normalized by construction and depend on source power, so that uncoupled sources with high power profiles may become false positives. This limitation motivated us to develop a statistical test based on randomization of original time series or cross-spectrum in such a way that power distribution in source space is preserved, but phase interactions are eliminated. The generation of covariance matrices from Wishart distribution appeared to be the most reliable method, when applied to data from simulations. Thus, together with the proposed statistical test PSIICOS can be used as an effective instrument applicable to real EEG- or MEG-data in fundamental research or for clinical purposes.

Brain-computer interfaces are considered as the next level of human-machine interaction. A bunch of approaches in decoding human states aims to achieve sufficient precision and accommodate a growing number of distinct states to decode. The following study investigates the capabilities of the EEG-based inverse modelling to improve the classification accuracy and provides a comparison between different inverse models. The computational pipeline of represented BCI includes clustering and dimension reduction of the forward model. The obtained results show the advantages of minimal norm estimate (MNE) inverse operator in comparison to the Beamformer, sLORETA. We have also observed that a motor imagery BCI based on the fully blown individual inverse model outperformed that based on Riemann geometry-based approaches, while the latter demonstrated performance superior to the approaches using band specific sensor space power distribution. The performance analysis was done using a 32 channel EEG data recorded during motor imagery of the four limbs.

Objective. Epilepsy is a widely spread neurological disease, whose treatment often requires resection of the pathological cortical tissue. Interictal spike analysis observed in the non-invasively collected EEG or MEG data offers an attractive way to localize epileptogenic cortical structures for surgery planning purposes. Interictal spike detection in lengthy multichannel data is a daunting task that is still often performed manually. This frequently limits such an analysis to a small portion of the data which renders the appropriate risks of missing the potentially epileptogenic region. While a plethora of automatic spike detection techniques have been developed each with its own assumptions and limitations, none of them is ideal and the best results are achieved when the output of several automatic spike detectors are combined. This is especially true in the low signal-to-noise ratio conditions. To this end we propose a novel biomimetic approach for automatic spike detection based on a constrained mixed spline machinery that we dub as fast parametric curve matching (FPCM). Approach. Using the peak-wave shape parametrization, the constrained parametric morphological model is constructed and convolved with the observed multichannel data to efficiently determine mixed spline parameters corresponding to each time-point in the dataset. Then the logical predicates that directly map to verbalized text-book like descriptions of the expected interictal event morphology allow us to accomplish the spike detection task. Main results. The results of simulations mimicking typical low SNR scenarios show the robustness and high receiver operating characteristic AUC values of the FPCM method as compared to the spike detection performed using more conventional approaches such as wavelet decomposition, template matching or simple amplitude thresholding. Applied to the real MEG and EEG data from the human patients and to rat ECoG data, the FPCM technique demonstrates reliable detection of the interictal events and localization of epileptogenic zones concordant with independent conclusions made by the epileptologist. Significance. Since the FPCM is computationally light, tolerant to high amplitude artifacts and flexible to accommodate verbalized descriptions of an arbitrary target morphology, it is likely to complement the existing arsenal of means for analysis of noisy interictal datasets.

In this work, we motivate and present a novel compact CNN. For the architectures that combine the adaptation in both space and time, we describen a theoretically justified approach to interpreting the temporal and spatial weights. We apply the proposed architecture to Berlin BCI IV competition and our own datasets to decode electrocorticogram into finger kinematics. Without feature engineering our architecture delivers similar or better decoding accuracy as compared to the BCI competition winner. After training the network, we interpret the solution (spatial and temporal convolution weights) and extract physiologically meaningful patterns.

Objective. Brain–computer interfaces (BCIs) decode information from neural activity and send it to external devices. The use of Deep Learning approaches for decoding allows for automatic feature engineering within the specific decoding task. Physiologically plausible interpretation of the network parameters ensures the robustness of the learned decision rules and opens the exciting opportunity for automatic knowledge discovery. Approach. We describe a compact convolutional network-based architecture for adaptive decoding of electrocorticographic (ECoG) data into finger kinematics. We also propose a novel theoretically justified approach to interpreting the spatial and temporal weights in the architectures that combine adaptation in both space and time. The obtained spatial and frequency patterns characterizing the neuronal populations pivotal to the specific decoding task can then be interpreted by fitting appropriate spatial and dynamical models. Main results. We first tested our solution using realistic Monte-Carlo simulations. Then, when applied to the ECoG data from Berlin BCI competition IV dataset, our architecture performed comparably to the competition winners without requiring explicit feature engineering. Using the proposed approach to the network weights interpretation we could unravel the spatial and the spectral patterns of the neuronal processes underlying the successful decoding of finger kinematics from an ECoG dataset. Finally we have also applied the entire pipeline to the analysis of a 32-channel EEG motor-imagery dataset and observed physiologically plausible patterns specific to the task. Significance. We described a compact and interpretable CNN architecture derived from the basic principles and encompassing the knowledge in the field of neural electrophysiology. For the first time in the context of such multibranch architectures with factorized spatial and temporal processing we presented theoretically justified weights interpretation rules. We verified our recipes using simulations and real data and demonstrated that the proposed solution offers a good decoder and a tool for investigating motor control neural mechanisms.

In a recent review, Vyas et al. commented on our previous observations regarding the presence of response sequences in the activity of cortical neuronal population and the contribution of such sequences to rotational dynamics patterns revealed with jPCA. Vyas et al. suggested that rotations generated from sequence-like responses are different from the ones arising from empirical neuronal patterns, which are highly heterogeneous across motor conditions in terms of response timing and shape. Here we extend our previous findings with new results showing that empirical population data contain plentiful neuronal responses whose shape and timing persist across arm-movement conditions. The more complex, heterogeneous responses can be also found; these response patterns also contain temporal sequences, which are evident from the analysis of cross-condition variance. Combined with simulation results, these observations show that both consistent and heterogeneous responses contribute to rotational patterns revealed with jPCA. We suggest that the users of jPCA should consider these two contributions when interpreting their results. Overall, we do not see any principal contradiction between the neural population dynamics framework and our results pertaining to sequence-like responses. Yet, questions remain regarding the conclusions that can be drawn from the analysis of low-dimensional representations of neuronal population data.

A P300 brain-computer interface (BCI) is a paradigm, where text characters are decoded from visual evoked potentials (VEPs). In a popular implementation, called P300 speller, a subject looks at a display where characters are flashing and selects one character by attending to it. The selection is recognized by the strongest VEP. The speller performs well when cortical responses to target and non-target stimuli are sufficiently different. Although many strategies have been proposed for improving the spelling, a relatively simple one received insufficient attention in the literature: reduction of the visual field to diminish the contribution from non-target stimuli. Previously, this idea was implemented in a single-stimulus switch that issued an urgent command. To tackle this approach further, we ran a pilot experiment where ten subjects first operated a traditional P300 speller and then wore a binocular aperture that confined their sight to the central visual field. Visual field restriction resulted in a reduction of non-target responses in all subjects. Moreover, in four subjects, target-related VEPs became more distinct. We suggest that this approach could speed up BCI operations and reduce user fatigue. Additionally, instead of wearing an aperture, non-targets could be removed algorithmically or with a hybrid interface that utilizes an eye tracker. We further discuss how a P300 speller could be improved by taking advantage of the different physiological properties of the central and peripheral vision. Finally, we suggest that the proposed experimental approach could be used in basic research on the mechanisms of visual processing.

Objective. Feedback latency was shown to be a critical parameter in a range of applications that imply learning. The therapeutic effects of neurofeedback (NFB) remain controversial. We hypothesized that often encountered unreliable results of NFB intervention could be associated with large feedback latency values that are often uncontrolled and may preclude the efficient learning. Approach. We engaged our subjects into a parietal alpha power unpregulating paradigm facilitated by visual NFB based on the individually extracted envelope of the alpha-rhythm at P4 electrode. NFB was displayed either as soon as electroencephalographic (EEG) envelope was processed, or with an extra 250 or 500 ms delay. The feedback training consisted of 15 two-minute long blocks interleaved with 15 s pauses. We have also recorded 2 min long baselines immediately before and after the training. Main results. The time course of NFB-induced changes in the alpha rhythm power clearly depended on NFB latency, as shown with the adaptive Neyman test. NFB had a strong effect on the alpha-spindle incidence rate, but not on their duration or amplitude. The sustained changes in alpha activity measured after the completion of NFB training were negatively correlated to latency, with the maximum change for the shortest tested latency and no change for the longest. Significance. Here we for the first time show that visual NFB of parietal EEG alpha-activity is efficient only when delivered to human subjects at short latency, which guarantees that NFB arrives when an alpha spindle is still ongoing. Such a considerable effect of NFB latency on the alpha-activity temporal structure could explain some of the previous inconsistent results, where latency was neither controlled nor documented. Clinical practitioners and manufacturers of NFB equipment should add latency to their specifications while enabling latency monitoring and supporting short-latency operations.

Magnetoencephalography (MEG) is a neuroimaging method ideally suited for non-invasive studies of brain dynamics. MEG’s spatial resolution critically depends on the approach used to solve the ill-posed inverse problem in order to transform sensor signals into cortical activation maps. Over recent years non-globally optimized solutions based on the use of adaptive beamformers (BF) gained popularity. When operating in the environment with a small number of uncorrelated sources the BFs perform optimally and yield high spatial resolution. However, the BFs are known to fail when dealing with correlated sources acting like poorly tuned spatial filters with low signal-to-noise ratio (SNR) of the output timeseries and often meaningless cortical maps of power distribution. This fact poses a serious limitation on the broader use of this promising technique especially since fundamental mechanisms of brain functioning, its inherent symmetry and task-based experimental paradigms result into a great deal of correlation in the activity of cortical sources. To cope with this problem, we developed a novel data covariance modification approach that allows for building beamformers that maintain high spatial resolution when operating in the environments with correlated sources. At the core of our method is a projection operation applied to the vectorized sensor-space covariance matrix. This projection does not remove the activity of the correlated sources from the sensor-space covariance matrix but rather selectively handles their contributions to the covariance matrix and creates a sufficiently accurate approximation of an ideal data covariance that could hypothetically be observed should these sources be uncorrelated. Since the projection operation is reciprocal to the PSIICOS method developed by us earlier (Ossadtchi et al., 2018) we refer to the family of algorithms presented here as ReciPSIICOS. We assess the performance of the novel approach using realistically simulated MEG data and show its superior performance in comparison to the classical BF approaches and well established MNE as a method immune to source synchrony by design. We have also applied our approach to the MEG datasets from the two experiments involving two different auditory tasks. The analysis of experimental MEG datasets showed that beamformers from ReciPSIICOS family, but not the classical BF, discovered the expected bilateral focal sources in the primary auditory cortex and detected motor cortex activity associated with the audio-motor task. In most cases MNE managed well but as expected produced more spatially diffuse source distributions. Notably, ReciPSIICOS beamformers yielded cortical activity estimates with SNR several times higher than that obtained with the classical BF, which may indirectly indicate the severeness of the signal cancellation problem when applying classical beamformers to MEG signals generated by synchronous sources.

Brain-computer interfaces (BCIs) decode information from neural activity and send it to external devices. In recent years, we have seen an emergence of new algorithms for BCI decoding including those based on the deep-learning principles. Here we describe a compact convolutional network-based architecture for adaptive decoding of electrocorticographic (ECoG) data into finger kinematics. We also propose a theoretically justified approach to interpreting the spatial and temporal weights in the architectures that combine adaptation in both space and time, such as the one described here. In these architectures the weights are optimized not only to align with the target sources but also to tune away from the interfering ones, in both the spatial and the frequency domains. The obtained spatial and frequency patterns characterizing the neuronal populations pivotal to the specific decoding task can then be interpreted by fitting appropriate spatial and dynamical models.We first tested our solution using realistic Monte-Carlo simulations. Then, when applied to the ECoG data from Berlin BCI IV competition dataset, our architecture performed comparably to the competition winners without requiring explicit feature engineering. Moreover, using the proposed approach to the network weights interpretation we could unravel the spatial and the spectral patterns of the neuronal processes underlying the successful decoding of finger kinematics from another ECoG dataset with known sensor positions.As such, the proposed solution offers a good decoder and a tool for investigating neural mechanisms of motor control.

Epilepsy is one of the most common neurological disorders, with about 30% of cases being drug-resistant and requiring surgical intervention. To localize the epileptogenic zone (EZ), the pathological area that has to be surgically removed, brain regions are inspected for the presence of spikes during the interictal periods. This procedure maps irritative zones where spikes are present, but it is still challenging to determine which of the irritative zones generate seizures. To localize the source of seizures more precisely, a large-scale approach could be applied where the causal relationship is assessed between the signals recorded in a finite number of irritative zones [27]. This method however, does not reveal the fine-grained spatiotemporal patterns of spikes, which could provide valuable information regarding EZ location and increase the likelihood of surgery success [33].Here we present a framework to noninvasively investigate the fine patterns of interictal spikes present in magnetoencephalographic (MEG) data. We use a traveling wave model, previously employed in the analysis of cortical alpha oscillations [16], to regularize the MEG inverse problem and to determine the cortical paths of spike traveling waves. Our algorithm represents spike propagation patterns as a superposition of local waves traveling along radial paths stemming from a single origin. With the help of the positively constrained LASSO technique we scan over wave onset moment and propagation velocity parameters to determine their combination that yields the best fit to the MEG sensor data of each spike.We first used realistically simulated MEG data to validate the algorithm ability to successfully track interictal activity on a millimeter-millisecond scale. Next, we examined MEG data from three patients with drug-resistant epilepsy. Wave-like spike patterns with clear propagation dynamics were found in a fraction of spikes, whereas the other fraction could not be explained by the wave propagation model with a small number of propagation directions. Moreover, in agreement with the previous work [33], the spike waves with clear propagation dynamics exhibited spatial segregation and matched the clinical records on seizure onset zones (SOZs) available for two patients out of three.

Handwriting is an advanced motor skill and one of the key developments in human culture. Here we show that handwriting can be decoded—offline and online—from electromyographic (EMG) signals recorded from multiple hand and forearm muscles. We convert EMGs into continuous handwriting traces and into discretely decoded font characters. For this purpose, we use Wiener and Kalman filters, and machine learning algorithms. Our approach is applicable to clinical neural prostheses for restoration of dexterous hand movements, and to medical diagnostics of neural disorders that affect handwriting. We also propose that handwriting could be decoded from cortical activity, such as the activity recorded with electrocorticography (ECoG).

Closed-loop Neuroscience is based on the experimental approach where the ongoing brain activity is recorded, processed, and passed back to the brain as sensory feedback or direct stimulation of neural circuits. The artificial closed loops constructed with this approach expand the traditional stimulus-response experimentation. As such, closed-loop Neuroscience provides insights on the function of loops existing in the brain and the ways the flow of neural information could be modified to treat neurological conditions.Neural oscillations, or brain rhythms, are a class of neural activities that have been extensively studied and also utilized in brain rhythm-contingent (BRC) paradigms that incorporate closed loops. In these implementations, instantaneous power and phase of neural oscillations form the signal that is fed back to the brain.Here we addressed the problem of feedback delay in BRC paradigms. In many BRC systems, it is critical to keep the delay short. Long delays could render the intended modification of neural activity impossible because the stimulus is delivered after the targeted neural pattern has already completed. Yet, the processing time needed to extract oscillatory components from the broad-band neural signals can significantly exceed the period of oscillations, which puts a demand for algorithms that could minimize the delay.We used EEG data collected in human subjects to systematically investigate the performance of a range of signal processing methods in the context of minimizing delay in BRC systems. We proposed a family of techniques based on the least-squares filter design – a transparent and simple approach, as it required a single parameter to adjust the accuracy versus latency trade-off. Our algorithm performed on par or better than the state-of the art techniques currently used for the estimation of rhythm envelope and phase in closed-loop EEG paradigms.

Back in 2012, Churchland and his colleagues proposed that “rotational dynamics”, uncovered through linear transformations of multidimensional neuronal data, represent a fundamental type of neuronal population processing in a variety of organisms, from the isolated leech central nervous system to the primate motor cortex. Here, we evaluated this claim using Churchland’s own data and simple simulations of neuronal responses. We observed that rotational patterns occurred in neuronal populations when (1) there was a temporal sequence in peak firing rates exhibited by individual neurons, and (2) this sequence remained consistent across different experimental conditions. Provided that such a temporal order of peak firing rates existed, rotational patterns could be easily obtained using a rather arbitrary computer simulation of neural activity; modeling of any realistic properties of motor cortical responses was not needed. Additionally, arbitrary traces, such as Lissajous curves, could be easily obtained from Churchland’s data with multiple linear regression. While these observations suggest that temporal sequences of neuronal responses could be visualized as rotations with various methods, we express doubt about Churchland et al.’s bold assessment that such rotations are related to “an unexpected yet surprisingly simple structure in the population response”, which “explains many of the confusing features of individual neural responses”. Instead, we argue that their approach provides little, if any, insight on the underlying neuronal mechanisms employed by neuronal ensembles to encode motor behaviors in any species., Scientific Reports, 9 (1), ISSN:2045-2322

Objective The rapidly developing paradigm of closed-loop neuroscience has extensively employed brain rhythms as the signal forming real-time neurofeedback, triggering brain stimulation, or governing stimulus selection. However, the efficacy of brain rhythm contingent paradigms suffers from significant delays related to the process of extraction of oscillatory parameters from broad-band neural signals with conventional methods. To this end, real-time algorithms are needed that would shorten the delay while maintaining an acceptable speed-accuracy trade-off. Approach Here we evaluated a family of techniques based on the application of the least-squares complex-valued filter (LSCF) design to real-time quantification of brain rhythms. These techniques allow for explicit optimization of the speed-accuracy trade-off when quantifying oscillatory patterns. We used EEG data collected from 10 human participants to systematically compare LSCF approach to the other commonly used algorithms. Each method being evaluated was optimized by scanning through the grid of its hyperparameters using independent data samples. Main results When applied to the task of estimating oscillatory envelope and phase, the LSCF techniques outperformed in speed and accuracy both conventional Fourier transform and rectification based methods as well as more advanced techniques such as those that exploit autoregressive extrapolation of narrow-band filtered signals. When operating at zero latency, the weighted LSCF approach yielded 75% accuracy when detecting alpha-activity episodes, as defined by the amplitude crossing of the 95th-percentile threshold. Significance The LSCF approaches are easily applicable to low-delay quantification of brain rhythms. As such, these methods are useful in a variety of neurofeedback, brain-computer-interface and other experimental paradigms that require rapid monitoring of brain rhythms.

Neurofeedback (NFB) is a real-time paradigm, where subjects monitor their own brain activity presented to them via one of the sensory modalities: visual, auditory or tactile. NFB has been proposed as an approach to treat neurological conditions and augment brain functions. In many applications, especially in the automatic learning scenario it is important to decrease NFB latency, so that appropriate brain mechanisms can be efficiently engaged. To this end, we propose a novel algorithm that significantly reduces feedback signal presentation in the electroencephalographic (EEG) NFB paradigm. The algorithm is based on the least squares optimization of the finite impulse response (FIR) filter weights and analytic signal reconstruction. In this approach, the trade-off between NFB latency and the accuracy of EEG envelope estimation can be achieved depending on the application needs. Moreover, the algorithm allows to implement predictive NFB by setting latency to negative values while maintaining acceptable envelope estimation accuracy. As such, our algorithm offers significant improvements in cases where subjects need to detect neural events as soon as possible and even in advance.

The principal possibility of creating optically pumped compact magnetic sensor for MEG operating in a wide magnetic field range is experimentally proved.

Increasing evidence suggests that neuronal communication is a defining property of functionally specialized brain networks and that it is implemented through synchronization between population activities of distinct brain areas. The detection of long-range coupling in electroencephalography (EEG) and magnetoencephalography (MEG) data using conventional metrics (such as coherence or phase-locking value) is by definition contaminated by spatial leakage. Methods such as imaginary coherence, phase-lag index or orthogonalized amplitude correlations tackle spatial leakage by ignoring zero-phase interactions. Although useful, these metrics will by construction lead to false negatives in cases where true zero-phase coupling exists in the data and will underestimate interactions with phase lags in the vicinity of zero. Yet, empirically observed neuronal synchrony in invasive recordings indicates that it is not uncommon to find zero or close-to-zero phase lag between the activity profiles of coupled neuronal assemblies. Here, we introduce a novel method that allows us to mitigate the undesired spatial leakage effects and detect zero and near zero phase interactions. To this end, we propose a projection operation that operates on sensor-space cross-spectrum and suppresses the spatial leakage contribution but retains the true zero-phase interaction component. We then solve the network estimation task as a source estimation problem defined in the product space of interacting source topographies. We show how this framework provides reliable interaction detection for all phase-lag values and we thus refer to the method as Phase Shift Invariant Imaging of Coherent Sources (PSIICOS). Realistic simulations demonstrate that PSIICOS has better detector characteristics than existing interaction metrics. Finally, we illustrate the performance of PSIICOS by applying it to real MEG dataset recorded during a standard mental rotation task. Taken together, using analytical derivations, data simulations and real brain data, this study presents a novel source-space MEG/EEG connectivity method that overcomes previous limitations and for the first time allows for the estimation of true zero-phase coupling via non-invasive electrophysiological recordings.

Spatial encoding in the hippocampus is multifactorial, and it is well established that metric information about space is conferred by place cells that fire when an animal finds itself in a specific environmental location. Visuospatial contexts comprise a key element in the formation of place fields. Nevertheless, hippocampus does not only use visual cues to generate spatial representations. In the absence of visual input, both humans and other vertebrates studied in this context, are capable of generating very effective spatial representations. However, little is known about the relationship between nonvisual sensory modalities and the establishment of place fields. Substantial evidence exists that olfactory information can be used to learn spatial contexts. Here, we report that learning about a distinct odor constellation in an environment, where visual and auditory cues are suppressed, results in stable place fields that rotate when the odor constellations are rotated and remap when the odor constellations are shuffled. These data support that the hippocampus can use nonvisuospatial resources, and specifically can use spatial olfactory information, to generate spatial representations. Despite the less precise nature of olfactory stimuli compared with visual stimuli, these can substitute for visual inputs to enable the acquisition of metric information about space.

The premotor cortex (PM) receives inputs from parietal cortical areas representing processed visuospatial information, translates that information into programs for particular movements, and communicates those programs to the primary motor cortex (M1) for execution. Consistent with this general function, intracortical microstimulation (ICMS) in the PM of sufficient frequency, amplitude, and duration has been shown to evoke complex movements of the arm and hand that vary systematically depending on the locus of stimulation. Using frequencies and amplitudes too low to evoke muscle activity, however, we found that ICMS in the PM can provide instructions to perform specific reach, grasp, and manipulate movements. These instructed actions were not fixed but rather were learned through associations between the arbitrary stimulation locations and particular movements. Low-amplitude ICMS at different PM locations thus evokes distinguishable experiences that can become associated with specific movements arbitrarily, providing a novel means of injecting information into the nervous system.

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.

We present a solution to employ brain-controlled biometric signals to operate external technical devices. Such signals include multiple electrode electroencephalographic (EEG) signals, electromyography (EMG) signals reflecting muscle contraction pattern, geometrical pattern of body limb kinematics, and other modalities. Being collected, properly decoded and interpreted, the signals can be used as a control or navigation signals of artificial machines, e.g., technical devices. Our interface solution based on a combination of signals of different modalities is capable to provide composite command and proportional multisite control of different technical devices with theoretically unlimited computational power. The feedback to the operator by visual channel or in virtual reality permits to compensate control errors and provides adaptive control. The control system can be implemented with wearable electronics. Examples of technical devices under the control are presented.

Humans often change their beliefs or behavior due to the behavior or opinions of others. This study explored with the use of human event related potentials (ERPs) whether social conformity is based on a general performance monitoring mechanism. We tested the hypothesis that conflicts with a normative group opinion evoke a feedback related negativity (FRN) often associated with performance monitoring and subsequent adjustment of behavior. The experimental results show that individual judgments of facial attractiveness were adjusted in line with a normative group opinion. A mismatch between individual and group opinions triggered a frontocentral negative deflection with the maximum at 200 ms similar to FRN. Overall a conflict with a normative group opinion triggered a cascade of neuronal responses: from an earlier FRN response reflecting a conflict with the normative opinion to a later ERP component (peaking at 380 ms) reflecting a conforming behavioral adjustment. These results add to the growing literature on neuronal mechanisms of social influence by disentangling the conflict monitoring signal in response to the perceived violation of social norms and the neural signal of a conforming behavioral adjustment. © The Author (2012). Published by Oxford University Press.

Connectivity measures are (typically bivariate) statistical measures that may be used to estimate interactions between brain regions from electrophysiological data. We review both formal and informal descriptions of a range of such measures, suitable for the analysis of human brain electrophysiological data, principally electro- and magnetoencephalography. Methods are described in the space-time, space-frequency, and space-time-frequency domains. Signal processing and information theoretic measures are considered, and linear and nonlinear methods are distinguished. A novel set of cross-time-frequency measures is introduced, including a cross-time-frequency phase synchronization measure.