False perspectives on human language: Why statistics needs linguistics

A sharp tension exists about the nature of human language between two opposite parties: those who believe that statistical surface distributions, in particular using measures like surprisal, provide a better understanding of language processing, vs. those who believe that discrete hierarchical structures implementing linguistic information such as syntactic ones are a better tool. In this paper, we show that this dichotomy is a false one. Relying on the fact that statistical measures can be defined on the basis of either structural or non-structural models, we provide empirical evidence that only models of surprisal that reflect syntactic structure are able to account for language regularities

Data-driven body–machine interface for the accurate control of drones

The teleoperation of nonhumanoid robots is often a demanding task, as most current control interfaces rely on mappings between the operator’s and the robot’s actions, which are determined by the design and characteristics of the interface, and may therefore be challenging to master. Here, we describe a structured methodology to identify common patterns in spontaneous interaction behaviors, to implement embodied user interfaces, and to select the appropriate sensor type and positioning. Using this method, we developed an intuitive, gesture-based control interface for real and simulated drones, which outperformed a standard joystick in terms of learning time and steering abilities. Implementing this procedure to identify body-machine patterns for specific applications could support the development of more intuitive and effective interfaces

EEG-Meta-Microstates: Towards a More Objective Use of Resting-State EEG Microstate Findings Across Studies.

Over the last decade, EEG resting-state microstate analysis has evolved from a niche existence to a widely used and well-accepted methodology. The rapidly increasing body of empirical findings started to yield overarching patterns of associations of biological and psychological states and traits with specific microstate classes. However, currently, this cross-referencing among apparently similar microstate classes of different studies is typically done by "eyeballing" of printed template maps by the individual authors, lacking a systematic procedure. To improve the reliability and validity of future findings, we present a tool to systematically collect the actual data of template maps from as many published studies as possible and present them in their entirety as a matrix of spatial similarity. The tool also allows importing novel template maps and systematically extracting the findings associated with specific microstate maps from ongoing or published studies. The tool also allows importing novel template maps and systematically extracting the findings associated with specific microstate maps in the literature. The analysis of 40 included sets of template maps indicated that: (i) there is a high degree of similarity of template maps across studies, (ii) similar template maps were associated with converging empirical findings, and (iii) representative meta-microstates can be extracted from the individual studies. We hope that this tool will be useful in coming to a more comprehensive, objective, and overarching representation of microstate findings

EXTRACTION OF EEG EMOTIVE RESPONSES BY MEANS OF A ICA DATA-DRIVEN APPROACH IN NONSTATIONARY CONDITION

Hypoxia is known to impair cognitive performance, alter perception and produce irreversible brain damage if protracted. To investigate its effects on emotion perception, event related potentials evoked by the presentation of emotional unpleasant aversive and neutral pictures during free breathing and breath holding (BH) were recorded. The experiment was performed by male diving athletes in air. This thesis work includes the complete signal analysis performed on the recorded data, which ranges from the preprocessing and artifacts removal to the development of innovative methods for feature extraction on highly noisy,multivariate and non-stationary processes. By means of a data-driven approach (ICASSO), suitable for disentangling the ERPs components deriving from independent brain sources, brain responses during the free breathing condition were modeled and the changes occurring during BH were described by amplitude-scalings and time-shiftings of the same sources. These components show brain response changes during both the normoxic and the hypoxic phases of BH related to individual brain sources and thus to the functioning of specific brain areas. The 1st component has a posterior localization, no changes during the BH phases were found indicating that the main EEG features of emotional processing are preserved throughout BH; two other components, lateralized, respectively active on the temporo-frontal and on the frontal midline regions, decrease in amplitude during BH with no differences between the normoxic and hypoxic phases. Finally, a component widespread on the scalp but with a frontal prevalence, shows an increasing reduction parallel to the hypoxic trend. The spatial localization of these components is compatible with a set of processing modules that affect automatic and intentional control of attention

Selecting the best number of synergies in gait: preliminary results on young and elderly people

Abstract—Matrix factorization algorithms are increasingly used to extract meaningful information from multivariate EMG datasets. However a key issue is the selection of the number of synergies (i.e., model order) to retain. In this preliminary work a set of criteria, based on Independent Component Analysis, was developed to determine the number of synergies to extract from a multivariate EMG dataset, and applied on EMG signals acquired from 12 leg muscles during walking at different cadences (40, 60,..., 140 strides per minute) in young and elderly subjects. The method was tested on ad-hoc created datasets with a predetermined number of embedded sources and amplitude of added noise. Young subjects walking patterns are explained by a number of synergies not significantly different with respect to elderly subjects. The inter-subject variability is greater at high (elderly) and low (young and elderly) cadences suggesting that the walking pattern is more stable at central frequencies. The type of preprocessing influences the number of underlying synergies: an increased number of independent components is needed to explain the variability of unfiltered data. The proposed method could serve as a guideline to scientists in the evaluation of walking performance. Further developments will include a validation of the method and its extension to other factorization algorithms. I

Applying dimension reduction to EEG data by Principal Component Analysis reduces the quality of its subsequent Independent Component decomposition

Independent Component Analysis (ICA) has proven to be an effective data driven method for analyzing EEG data, separating signals from temporally and functionally independent brain and non-brain source processes and thereby increasing their definition. Dimension reduction by Principal Component Analysis (PCA) has often been recommended before ICA decomposition of EEG data, both to minimize the amount of required data and computation time. Here we compared ICA decompositions of fourteen 72-channel single subject EEG data sets obtained (i) after applying preliminary dimension reduction by PCA, (ii) after applying no such dimension reduction, or else (iii) applying PCA only. Reducing the data rank by PCA (even to remove only 1% of data variance) adversely affected both the numbers of dipolar independent components (ICs) and their stability under repeated decomposition. For example, decomposing a principal subspace retaining 95% of original data variance reduced the mean number of recovered ‘dipolar’ ICs from 30 to 10 per data set and reduced median IC stability from 90% to 76%. PCA rank reduction also decreased the numbers of near-equivalent ICs across subjects. For instance, decomposing a principal subspace retaining 95% of data variance reduced the number of subjects represented in an IC cluster accounting for frontal midline theta activity from 11 to 5. PCA rank reduction also increased uncertainty in the equivalent dipole positions and spectra of the IC brain effective sources. These results suggest that when applying ICA decomposition to EEG data, PCA rank reduction should best be avoided