Adaptable and Robust EEG Bad Channel Detection Using Local Outlier Factor (LOF)

Electroencephalogram (EEG) data are typically affected by artifacts. The detection and removal of bad channels (i.e., with poor signal-to-noise ratio) is a crucial initial step. EEG data acquired from different populations require different cleaning strategies due to the inherent differences in the data quality, the artifacts' nature, and the employed experimental paradigm. To deal with such differences, we propose a robust EEG bad channel detection method based on the Local Outlier Factor (LOF) algorithm. Unlike most existing bad channel detection algorithms that look for the global distribution of channels, LOF identifies bad channels relative to the local cluster of channels, which makes it adaptable to any kind of EEG. To test the performance and versatility of the proposed algorithm, we validated it on EEG acquired from three populations (newborns, infants, and adults) and using two experimental paradigms (event-related and frequency-tagging). We found that LOF can be applied to all kinds of EEG data after calibrating its main hyperparameter: the LOF threshold. We benchmarked the performance of our approach with the existing state-of-the-art (SoA) bad channel detection methods. We found that LOF outperforms all of them by improving the F1 Score, our chosen performance metric, by about 40% for newborns and infants and 87.5% for adults

Temporal integration in visual word recognition

When two displays are presented in close temporal succession at the same location, how does the brain assign them to one versus two conscious percepts? We investigate this issue using a novel reading paradigm in which the odd and even letters of a string are presented alternatively at a variable rate. The results reveal a window of temporal integration during reading, with a nonlinear boundary around approximately 80 msec of presentation duration. Below this limit, the oscillating stimulus is easily fused into a single percept, with all characteristics of normal reading. Above this limit, reading times are severely slowed and suffer from a word-length effect. ERPs indicate that, even at the fastest frequency, the oscillating stimulus elicits synchronous oscillations in posterior visual cortices, while late ERP components sensitive to lexical status vanish beyond the fusion threshold. Thus, the fusion/segregation dilemma is not resolved by retinal or subcortical filtering, but at cortical level by at most 300 msec. The results argue against theories of visual word recognition and letter binding that rely on temporal synchrony or other fine temporal codes

Efficient Low-Frequency SSVEP Detection with Wearable EEG Using Normalized Canonical Correlation Analysis

Recent studies show that the integrity of core perceptual and cognitive functions may be tested in a short time with Steady-State Visual Evoked Potentials (SSVEP) with low stimulation frequencies, between 1 and 10 Hz. Wearable EEG systems provide unique opportunities to test these brain functions on diverse populations in out-of-the-lab conditions. However, they also pose significant challenges as the number of EEG channels is typically limited, and the recording conditions might induce high noise levels, particularly for low frequencies. Here we tested the performance of Normalized Canonical Correlation Analysis (NCCA), a frequency-normalized version of CCA, to quantify SSVEP from wearable EEG data with stimulation frequencies ranging from 1 to 10 Hz. We validated NCCA on data collected with an 8-channel wearable wireless EEG system based on BioWolf, a compact, ultra-light, ultra-low-power recording platform. The results show that NCCA correctly and rapidly detects SSVEP at the stimulation frequency within a few cycles of stimulation, even at the lowest frequency (4 s recordings are sufficient for a stimulation frequency of 1 Hz), outperforming a state-of-the-art normalized power spectral measure. Importantly, no preliminary artifact correction or channel selection was required. Potential applications of these results to research and clinical studies are discussed

A Dynamic Approach to the Thermodynamics of Superdiffusion

We address the problem of relating thermodynamics to mechanics in the case of microscopic dynamics without a finite time scale. The solution is obtained by expressing the Tsallis entropic index q as a function of the Levy index alpha, and using dynamical rather than probabilistic arguments.Comment: 4 pages, new revised version resubmitted to Phys. Rev. Let

Incidence rates of in-hospital carpal tunnel syndrome in the general population and possible associations with marital status

<p>Abstract</p> <p>Background</p> <p>Carpal tunnel syndrome (CTS) is a socially relevant condition associated with biomechanical risk factors. We evaluated age-sex-specific incidence rates of in-hospital cases of CTS in central/northern Italy and explored relations with marital status.</p> <p>Methods</p> <p>Seven regions were considered (overall population, 14.9 million) over 3–6-year periods between 1997 and 2002 (when out-of-hospital CTS surgery was extremely rare). Incidence rates of in-hospital cases of CTS were estimated based on 1) codified demographic, diagnostic and intervention data in obligatory discharge records from all Italian public/private hospitals, archived (according to residence) on regional databases; 2) demographic general population data for each region. We compared (using the χ_scoretest) age-sex-specific rates between married, unmarried, divorced and widowed subsets of the general population. We calculated standardized incidence ratios (SIRs) for married/unmarried men and women.</p> <p>Results</p> <p>Age-standardized incidence rates (per 100,000 person-years) of in-hospital cases of CTS were 166 in women and 44 in men (106 overall). Married subjects of both sexes showed higher age-specific rates with respect to unmarried men/women. SIRs were calculated comparing married vs unmarried rates of both sexes: 1.59 (95% confidence interval [95% CI], 1.57–1.60) in women, and 1.42 (95% CI, 1.40–1.45) in men. As compared with married women/men, widows/widowers both showed 2–3-fold higher incidence peaks during the fourth decade of life (beyond 50 years of age, widowed subjects showed similar trends to unmarried counterparts).</p> <p>Conclusion</p> <p>This large population-based study illustrates distinct age-related trends in men and women, and also raises the question whether marital status could be associated with CTS in the general population.</p

Response to metal stress of Nicotiana langsdorffii plants wild-type and transgenic for the rat glucocorticoid receptor gene

Recently our findings have shown that the integration of the gene coding for the rat gluco-corticoid receptor (GR receptor) in Nicotiana langsdorffii plants induced morphophysiological effects in transgenic plants through the modification of their hormonal pattern. Phytohormones play a key role in plant responses to many different biotic and abiotic stresses since a modified hormonal profile up-regulates the activation of secondary metabolites involved in the response to stress. In this work transgenic GR plants and isogenic wild type genotypes were exposed to metal stress by treating them with 30 ppm cadmium(II) or 50 ppm chromium(VI). Hormonal patterns along with changes in key response related metabolites were then monitored and compared. Heavy metal up-take was found to be lower in the GR plants. The transgenic plants exhibited higher values of S-abscisic acid (S-ABA) and 3-indole acetic acid (IAA), salicylic acid and total polyphenols, chlorogenic acid and antiradical activity, compared to the untransformed wild type plants. Both Cd and Cr treatments led to an increase in hormone concentrations and secondary metabolites only in wild type plants. Analysis of the results suggests that the stress responses due to changes in the plant's hormonal system may derive from the interaction between the GR receptor and phytosteroids, which are known to play a key role in plant physiology and development. (C) 2013 Elsevier GmbH. All rights reserved

Adaptation of the visual system to the temporal statistics of natural images

This thesis focuses on ways of dynamically adapting the visual system to the temporal statistics of natural images. Recent experiments suggest that adaptation relies on a coding strategy that has the effect of normalising the neuronal activity with respect to the standard deviation of the stimulus. We investigate the hypothesis that such a nonlinear coding strategy could represent a key mechanism in natural vision both for adaptation and efficient representation of the stimulus. We model the process of variance normalisation in a simple way, and we train it on time series mimicking the typical visual input of a human photoreceptor in a natural environment. Simulations confirm our hypothesis: variance normalisation adapts the wide natural range of light intensities into the limited neuronal one and efficiently removes almost all the redundancy present in the temporal structure of natural images. Moreover, despite the long-range correlations present in the natural input, the integration time corresponding to the optimal normalisation is surprisingly short and compatible with the one observed experimentally. The importance of this result seems to lie in the simplicity of the model. In order to prove its computational power, it is shown that it efficiently removes the redundancy of time series of very different nature - financial time series - also characterised by long-range correlations. This result suggests that such a method efficiently removes the redundancy of a wide variety of data, no matter how much correlated they are