Space-by-time non-negative matrix factorization for single-trial decoding of M/EEG activity

We develop a novel methodology for the single-trial analysis of multichannel time-varying neuroimaging signals. We introduce the space-by-time M/EEG decomposition, based on Non-negative Matrix Factorization (NMF), which describes single-trial M/EEG signals using a set of non-negative spatial and temporal components that are linearly combined with signed scalar activation coefficients. We illustrate the effectiveness of the proposed approach on an EEG dataset recorded during the performance of a visual categorization task. Our method extracts three temporal and two spatial functional components achieving a compact yet full representation of the underlying structure, which validates and summarizes succinctly results from previous studies. Furthermore, we introduce a decoding analysis that allows determining the distinct functional role of each component and relating them to experimental conditions and task parameters. In particular, we demonstrate that the presented stimulus and the task difficulty of each trial can be reliably decoded using specific combinations of components from the identified space-by-time representation. When comparing with a sliding-window linear discriminant algorithm, we show that our approach yields more robust decoding performance across participants. Overall, our findings suggest that the proposed space-by-time decomposition is a meaningful low-dimensional representation that carries the relevant information of single-trial M/EEG signals

Four not six: revealing culturally common facial expressions of emotion

As a highly social species, humans generate complex facial expressions to communicate a diverse range of emotions. Since Darwin’s work, identifying amongst these complex patterns which are common across cultures and which are culture-specific has remained a central question in psychology, anthropology, philosophy, and more recently machine vision and social robotics. Classic approaches to addressing this question typically tested the cross-cultural recognition of theoretically motivated facial expressions representing six emotions, and reported universality. Yet, variable recognition accuracy across cultures suggests a narrower cross-cultural communication, supported by sets of simpler expressive patterns embedded in more complex facial expressions. We explore this hypothesis by modelling the facial expressions of over 60 emotions across two cultures, and segregating out the latent expressive patterns. Using a multi-disciplinary approach, we first map the conceptual organization of a broad spectrum of emotion words by building semantic networks in two cultures. For each emotion word in each culture, we then model and validate its corresponding dynamic facial expression, producing over 60 culturally valid facial expression models. We then apply to the pooled models a multivariate data reduction technique, revealing four latent and culturally common facial expression patterns that each communicates specific combinations of valence, arousal and dominance. We then reveal the face movements that accentuate each latent expressive pattern to create complex facial expressions. Our data questions the widely held view that six facial expression patterns are universal, instead suggesting four latent expressive patterns with direct implications for emotion communication, social psychology, cognitive neuroscience, and social robotics

Editorial : Improving Neuroprosthetics Through Novel Techniques for Processing Electrophysiological Human Brain Signals

Non peer reviewe

Bank procyclicality and output: Issues and policies

The recent global financial crisis has highlighted the importance of the procyclicality of the financial sector. The procyclicality has transformed banks from mitigation mechanisms to amplifiers of changes in economic activity, potentially affecting financial stability and economic growth. The causes of procyclicality can be attributed to market imperfections and deviations from the efficient market hypothesis, while other factors including the Basel-type regulations, accounting standards and leverage have exacerbated it. Several suggestions have been forwarded to attenuate procyclicality, in the form of rules and discretion. They are presented here according to the factors they aim to alleviate. Some of the suggestions have been adopted under the Basel III framework, which explicitly addresses the procyclicality issue

Bank procyclicality and output: Issues and policies

The recent global financial crisis has highlighted the importance of the procyclicality of the financial sector. The procyclicality has transformed banks from mitigation mechanisms to amplifiers of changes in economic activity, potentially affecting financial stability and economic growth. The causes of procyclicality can be attributed to market imperfections and deviations from the efficient market hypothesis, while other factors including the Basel-type regulations, accounting standards and leverage have exacerbated it. Several suggestions have been forwarded to attenuate procyclicality, in the form of rules and discretion. They are presented here according to the factors they aim to alleviate. Some of the suggestions have been adopted under the Basel III framework, which explicitly addresses the procyclicality issue

Can spillover effects provide forecasting gains? The case of oil price volatility

We consider spillovers between oil price volatility and key uncertainty indicators. Adding to existing studies, we extend the applicability of the spillover index beyond economic inference, by generating forecasts of oil price volatility. Findings suggest that spillover effects do not contain significant predictive information. This in turn, raises critical questions regarding the usefulness of the spillover index for such task. However, it is critical to collect further evidence for the support of our findings

Can spillover effects provide forecasting gains? The case of oil price volatility

We consider spillovers between oil price volatility and key uncertainty indicators. Adding to existing studies, we extend the applicability of the spillover index beyond economic inference, by generating forecasts of oil price volatility. Findings suggest that spillover effects do not contain significant predictive information. This in turn, raises critical questions regarding the usefulness of the spillover index for such task. However, it is critical to collect further evidence for the support of our findings

Space-by-Time Modular Decomposition Effectively Describes Whole-Body Muscle Activity During Upright Reaching in Various Directions

The modular control hypothesis suggests that motor commands are built from precoded modules whose specific combined recruitment can allow the performance of virtually any motor task. Despite considerable experimental support, this hypothesis remains tentative as classical findings of reduced dimensionality in muscle activity may also result from other constraints (biomechanical couplings, data averaging or low dimensionality of motor tasks). Here we assessed the effectiveness of modularity in describing muscle activity in a comprehensive experiment comprising 72 distinct point-to-point whole-body movements during which the activity of 30 muscles was recorded. To identify invariant modules of a temporal and spatial nature, we used a space-by-time decomposition of muscle activity that has been shown to encompass classical modularity models. To examine the decompositions, we focused not only on the amount of variance they explained but also on whether the task performed on each trial could be decoded from the single-trial activations of modules. For the sake of comparison, we confronted these scores to the scores obtained from alternative non-modular descriptions of the muscle data. We found that the space-by-time decomposition was effective in terms of data approximation and task discrimination at comparable reduction of dimensionality. These findings show that few spatial and temporal modules give a compact yet approximate representation of muscle patterns carrying nearly all task-relevant information for a variety of whole-body reaching movements