Anxiety affects approximately 5-10% of the adult population worldwide, placing a large burden on the health systems. Despite its omnipresence and impact on mental and physical health, most of the individuals affected by anxiety do not receive appropriate treatment. Current research in the field of psychiatry emphasizes the need to identify and validate biological markers relevant to this condition. Neurophysiological preclinical studies are a prominent approach to determine brain rhythms that can be reliable markers of key features of anxiety. However, while neuroimaging research consistently implicated prefrontal cortex and subcortical structures, such as amygdala and hippocampus, in anxiety, there is still a lack of consensus on the underlying neurophysiological processes contributing to this condition. Methods allowing non-invasive recording and assessment of cortical processing may provide an opportunity to help identify anxiety signatures that could be used as intervention targets. In this study, we apply Source-Power Comodulation (SPoC) to electroencephalography (EEG) recordings in a sample of participants with different levels of trait anxiety. SPoC was developed to find spatial filters and patterns whose power comodulates with an external variable in individual participants. The obtained patterns can be interpreted neurophysiologically. Here, we extend the use of SPoC to a multi-subject setting and test its validity using simulated data with a realistic head model. Next, we apply our SPoC framework to resting state EEG of 43 human participants for whom trait anxiety scores were available. SPoC inter-subject analysis of narrow frequency band data reveals neurophysiologically meaningful spatial patterns in the theta band (4-7 Hz) that are negatively correlated with anxiety. The outcome is specific to the theta band and not observed in the alpha (8-12 Hz) or beta (13-30 Hz) frequency range. The theta-band spatial pattern is primarily localised to the superior frontal gyrus. We discuss the relevance of our spatial pattern results for the search of biomarkers for anxiety and their application in neurofeedback studies.

Periodic signals called Steady-State Visual Evoked Potentials (SSVEP) are elicited in the brain by flickering stimuli. They are usually detected by means of regression techniques that need relatively long trial lengths to provide feedback and/or sufficient number of calibration trials to be reliably estimated in the context of brain-computer interface (BCI). Thus, for BCI systems designed to operate with SSVEP signals, reliability is achieved at the expense of speed or extra recording time. Furthermore, regardless of the trial length, calibration free regression-based methods have been shown to suffer from significant performance drops when cognitive perturbations are present affecting the attention to the flickering stimuli. In this study we present a novel technique called Oscillatory Source Tensor Discriminant Analysis (OSTDA) that extracts oscillatory sources and classifies them using the newly developed tensor-based discriminant analysis with shrinkage. The proposed approach is robust for small sample size settings where only a few calibration trials are available. Besides, it works well with both low- and high-number-of-channel settings, using trials as short as one second. OSTDA performs similarly or significantly better than other three benchmarked state-of-the-art techniques under different experimental settings, including those with cognitive disturbances (i.e. four datasets with control, listening, speaking and thinking conditions). Overall, in this paper we show that OSTDA is the only pipeline among all the studied ones that can achieve optimal results in all analyzed conditions.

Ongoing oscillations and evoked responses are two main types of neuronal activity obtained with diverse electrophysiological recordings (EEG/MEG/iEEG/LFP). Although typically studied separately, they might in fact be closely related. One possibility to unite them is to demonstrate that neuronal oscillations have non-zero mean which predicts that stimulus- or task-triggered amplitude modulation of oscillations can contribute to the generation of evoked responses. We validated this mechanism using computational modelling and analysis of a large EEG data set. With a biophysical model, we indeed demonstrated that intracellular currents in the neuron are asymmetric and, consequently, the mean of alpha oscillations is non-zero. To understand the effect that neuronal currents exert on oscillatory mean, we varied several biophysical and morphological properties of neurons in the network, such as voltage-gated channel densities, length of dendrites, and intensity of incoming stimuli. For a very large range of model parameters, we observed evidence for non-zero mean of oscillations. Complimentary, we analysed empirical rest EEG recordings of 90 participants (50 young, 40 elderly) and, with spatio-spectral decomposition, detected at least one spatially-filtred oscillatory component of non-zero mean alpha oscillations in 93% of participants. In order to explain a complex relationship between the dynamics of amplitude-envelope and corresponding baseline shifts, we performed additional simulations with simple oscillators coupled with different time delays. We demonstrated that the extent of spatial synchronisation may obscure macroscopic estimation of alpha rhythm modulation while leaving baseline shifts unchanged. Overall, our results predict that amplitude modulation of neural oscillations should at least partially explain the generation of evoked responses. Therefore, inference about changes in evoked responses with respect to cognitive conditions, age or neuropathologies should be constructed while taking into account oscillatory neuronal dynamics.

Anxiety is one of the most prevalent mental disorders, affecting approximately 5-10% of the adult population worldwide. It can severely impact quality of life, but also place a large burden on the health systems. Despite its omnipresence and impact on mental and physical health, most of the individuals suffering from anxiety do not receive appropriate treatment. Furthermore, while neuroimaging research consistently implicated subcortical structures such as amygdala, hippocampus and prefrontal cortex in anxiety, there is still a lack of consensus on the underlying neurophysiological processes contributing to this condition. Thus, the objective neurophysiological markers for anxiety remain elusive. Methods allowing non-invasive recording and assessment of cortical processing provide an opportunity to help identify anxiety signatures that could be used as intervention targets. In this paper, we tackle this problem by applying a regression spatial filter called Source-Power Comodulation (SPoC) to trait anxiety data of 43 individuals. By maximizing the correlation of alpha band power and the level of trait anxiety in resting state electroencephalography (EEG) we are able to obtain neurophysiologically meaningful patterns that should be helpful in the search of biomarkers for mental disorders.

Neuronal oscillations are ubiquitous in the human brain and are implicated in virtually all brain functions. Although they can be described by a prominent peak in the power spectrum, their waveform is not necessarily sinusoidal and shows rather complex morphology. Both frequency and temporal descriptions of such non-sinusoidal neuronal oscillations can be utilized. However, in non-invasive EEG/MEG recordings the waveform of oscillations often takes a sinusoidal shape which in turn leads to a rather oversimplified view on oscillatory processes. In this study, we show in simulations how spatial synchronization can mask non-sinusoidal features of the underlying rhythmic neuronal processes. Consequently, the degree of non-sinusoidality can serve as a measure of spatial synchronization. To confirm this empirically, we show that a mixture of EEG components is indeed associated with more sinusoidal oscillations compared to the waveform of oscillations in each constituent component. Using simulations, we also show that the spatial mixing of the non-sinusoidal neuronal signals strongly affects the amplitude ratio of the spectral harmonics constituting the waveform. Finally, our simulations show how spatial mixing can affect the strength and even the direction of the amplitude coupling between constituent neuronal harmonics at different frequencies. Validating these simulations, we also demonstrate these effects in real EEG recordings. Our findings have far reaching implications for the neurophysiological interpretation of spectral profiles, cross-frequency interactions, as well as for the unequivocal determination of oscillatory phase.

Emotional arousal is a key component of a user's experience in immersive virtual reality (VR). Subjective and highly dynamic in nature, emotional arousal involves the whole body and particularly the brain. However, it has been difficult to relate subjective emotional arousal to an objective, neurophysiological marker - especially in naturalistic settings. We tested the association between continuously changing states of emotional arousal and oscillatory power in the brain during a VR roller coaster experience. We used novel spatial filtering approaches to predict self-reported emotional arousal from the electroencephalogram (EEG) signal of 38 participants. Periods of high vs. low emotional arousal could be classified with accuracies significantly above chance level. Our results are consistent with prior findings regarding emotional arousal in less naturalistic settings. We demonstrate a new approach to decode states of subjective emotional arousal from continuous EEG data in an immersive VR experience.

This work presents a variation of canonical correlation analysis (CCA), where the correlation coefficient between the instantaneous power of the projections is maximized, rather than between the projections themselves. The resulting optimization problem is not convex, and we have to resort to a sub-optimal approach. Concretely, we propose a two-step solution consisting of the singular value decomposition (SVD) of a “coherence” matrix followed by a rank-one matrix approximation. This technique is applied to blindly recovering signals in a model that is motivated by the study of neuronal dynamics in humans using electroencephalography (EEG) and magnetoencephalography (MEG). A distinctive feature of this model is that it allows recovery of amplitude-amplitude coupling between neuronal processes.

We introduce a novel framework for estimating vector fields using sparse basis field expansions (S-FLEX). The notion of basis fields, which are an extension of scalar basis functions, arises naturally in our framework from a rotational invariance requirement. We consider a regression setting as well as inverse problems. All variants discussed lead to second-order cone programming formulations. While our framework is generally applicable to any type of vector field, we focus in this paper on applying it to solving the EEG/MEG inverse problem. It is shown that significantly more precise and neurophysiologically more plausible location and shape estimates of cerebral current sources from EEG/MEG measurements become possible with our method when comparing to the state-of-the-art.

Brain-Computer Interfaces can suffer from a large variance of the subject condi- tions within and across sessions. For example vigilance fluctuations in the indi- vidual, variable task involvement, workload etc. alter the characteristics of EEG signals and thus challenge a stable BCI operation. In the present work we aim to define features based on a variant of the common spatial patterns (CSP) algorithm that are constructed invariant with respect to such nonstationarities. We enforce invariance properties by adding terms to the denominator of a Rayleigh coefficient representation of CSP such as disturbance covariance matrices from fluctuations in visual processing. In this manner physiological prior knowledge can be used to shape the classification engine for BCI. As a proof of concept we present a BCI classifier that is robust to changes in the level of parietal a -activity. In other words, the EEG decoding still works when there are lapses in vigilance.