GUIDER: a GUI for semiautomatic, physiologically driven EEG feature selection for a rehabilitation BCI

GUIDER is a graphical user interface developed in MATLAB software environment to identify electroencephalography (EEG)-based brain computer interface (BCI) control features for a rehabilitation application (i.e. post-stroke motor imagery training). In this context, GUIDER aims to combine physiological and machine learning approaches. Indeed, GUIDER allows therapists to set parameters and constraints according to the rehabilitation principles (e.g. affected hemisphere, sensorimotor relevant frequencies) and foresees an automatic method to select the features among the defined subset. As a proof of concept, we compared offline performances between manual, just based on operator’s expertise and experience, and GUIDER semiautomatic features selection on BCI data collected from stroke patients during BCI-supported motor imagery training. Preliminary results suggest that this semiautomatic approach could be successfully applied to support the human selection reducing operator dependent variability in view of future multi-centric clinical trials

Interfacce cervello-computer per la comunicazione aumentativa: algoritmi asincroni e adattativi e validazione con utenti finali

This thesis aimed at addressing some of the issues that, at the state of the art, avoid the P300-based brain computer interface (BCI) systems to move from research laboratories to end users’ home. An innovative asynchronous classifier has been defined and validated. It relies on the introduction of a set of thresholds in the classifier, and such thresholds have been assessed considering the distributions of score values relating to target, non-target stimuli and epochs of voluntary no-control. With the asynchronous classifier, a P300-based BCI system can adapt its speed to the current state of the user and can automatically suspend the control when the user diverts his attention from the stimulation interface. Since EEG signals are non-stationary and show inherent variability, in order to make long-term use of BCI possible, it is important to track changes in ongoing EEG activity and to adapt BCI model parameters accordingly. To this aim, the asynchronous classifier has been subsequently improved by introducing a self-calibration algorithm for the continuous and unsupervised recalibration of the subjective control parameters. Finally an index for the online monitoring of the EEG quality has been defined and validated in order to detect potential problems and system failures. This thesis ends with the description of a translational work involving end users (people with amyotrophic lateral sclerosis-ALS). Focusing on the concepts of the user centered design approach, the phases relating to the design, the development and the validation of an innovative assistive device have been described. The proposed assistive technology (AT) has been specifically designed to meet the needs of people with ALS during the different phases of the disease (i.e. the degree of motor abilities impairment). Indeed, the AT can be accessed with several input devices either conventional (mouse, touchscreen) or alterative (switches, headtracker) up to a P300-based BCI.Questa tesi affronta alcune delle problematiche che, allo stato dell'arte, limitano l'usabilità delle interfacce cervello computer (Brain Computer Interface - BCI) al di fuori del contesto sperimentale. E' stato inizialmente definito e validato un classificatore asincrono. Quest'ultimo basa il suo funzionamento sull'inserimento di un set di soglie all'interno del classificatore. Queste soglie vengono definite considerando le distribuzioni dei valori di score relativi agli stimoli target e non-target e alle epoche EEG in cui il soggetto non intendeva effettuare nessuna selezione (no-control). Con il classificatore asincrono, un BCI basato su potenziali P300 può adattare la sua velocità allo stato corrente dell'utente e sospendere automaticamente il controllo quando l'utente non presta attenzione alla stimolazione. Dal momento che i segnali EEG sono non-stazionari e mostrano una variabilità intrinseca, al fine di rendere possibile l'utilizzo dei sistemi BCI sul lungo periodo, è importante rilevare i cambiamenti dell'attività EEG e adattare di conseguenza i parametri del classificatore. A questo scopo, il classificatore asincrono è stato successivamente migliorato introducendo un algoritmo di autocalibrazione per la continua e non supervisionata ricalibrazione dei parametri di controllo soggettivi. Infine è stato definito e validato un indice per monitorare on-line la qualità del segnale EEG, in modo da rilevare potenziali problemi e malfunzionamenti del sistema. Questa tesi si conclude con la descrizione di un lavoro che ha coinvolto gli utenti finali (persone affette da sclerosi laterale amiotrofica-SLA). In particolare, basandosi sui principi dell’user-centered design, sono state descritte le fasi relative alla progettazione, sviluppo e validazione di una tecnologia assistiva (TA) innovativa. La TA è stata specificamente progettata per rispondere alla esigenze delle persone affetta da SLA durante le diverse fasi della malattia. Infatti, la TA proposta può essere utilizzata sia mediante dispositivi d’input tradizionali (mouse, tastiera) che alternativi (bottoni, headtracker) fino ad arrivare ad un BCI basato su potenziali P300

A no-reference metric for demosaicing artifacts that fits psycho-visual experiments

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Locating and Sizing Electric Vehicle Chargers Considering Multiple Technologies

In order to foster electric vehicle (EV) adoption rates, the availability of a pervasive and efficient charging network is a crucial requirement. In this paper, we provide a decision support tool for helping policymakers to locate and size EV charging stations. We consider a multi-year planning horizon, taking into account different charging technologies and different time periods (day and night). Accounting for these features, we propose an optimization model that minimizes total investment costs while ensuring a predetermined adequate level of demand coverage. In particular, the setup of charging stations is optimized every year, allowing for an increase in the number of chargers installed at charging stations set up in previous years. We have developed a tailored heuristic algorithm for the resulting problem. We validated our algorithm using case study instances based on the village of Gardone Val Trompia (Italy), the city of Barcelona (Spain), and the country of Luxembourg. Despite the variability in the sizes of the considered instances, our algorithm consistently provided high-quality results in short computational times, when compared to a commercial MILP solver. Produced solutions achieved optimality gaps within 7.5% in less than 90 s, often achieving computational times of less than 5 s

Region-Based Illuminant Estimation for Effective Color Correction

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Searching through photographic databases with QuickLook

G. Ciocca, C. Cusano, R. Schettini, S. Santini, A. de Polo, F. Tavanti, “Searching through photographic databases with QuickLook”. Proc. Multimedia on Mobile Devices 2012; and Multimedia Content Access: Algorithms and Systems VI. Ed- Reiner Creutzburg; David Akopian; Cees G. M. Snoek; Nicu Sebe; Lyndon Kennedy. 8304. 83040V-1 (2012). Copyright 2012 Society of Photo‑Optical Instrumentation Engineers. One print or electronic copy may be made for personal use only. Systematic reproduction and distribution, duplication of any material in this paper for a fee or for commercial purposes, or modification of the content of the paper are prohibited.We present here the results obtained by including a new image descriptor, that we called prosemantic feature vector, within the framework of QuickLook2 image retrieval system. By coupling the prosemantic features and the relevance feedback mechanism provided by QuickLook2, the user can move in a more rapid and precise way through the feature space toward the intended goal. The prosemantic features are obtained by a two-step feature extraction process. At the first step, low level features related to image structure and color distribution are extracted from the images. At the second step, these features are used as input to a bank of classifiers, each one trained to recognize a given semantic category, to produce score vectors. We evaluated the efficacy of the prosemantic features under search tasks on a dataset provided by Fratelli Alinari Photo Archive.© (2012) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only

High-throughput genotyping of resilient tomato landraces to detect candidate genes involved in the response to high temperatures

The selection of tolerant varieties is a powerful strategy to ensure highly stable yield under elevated temperatures. In this paper, we report the phenotypic and genotypic characterization of 10 tomato landraces to identify the best performing under high temperatures. The phenotyping of five yield-related traits allowed us to select one genotype that exhibits highly stable yield performances in different environmental conditions. Moreover, a Genotyping-by-Sequencing approach allowed us to explore the genetic variability of the tested genotypes. The high and stable yielding landrace E42 was the most polymorphic one, with ~49% and ~47% private SNPs and InDels, respectively. The effect of 26,113 mutations on proteins’ structure was investigated and it was discovered that 37 had a high impact on the structure of 34 proteins of which some are putatively involved in responses to high temperatures. Additionally, 129 polymorphic sequences aligned against tomato wild species genomes revealed the presence in the genotype E42 of several introgressed regions deriving from S. pimpinellifolium. The position on the tomato map of genes affected by moderate and high impact mutations was also compared with that of known markers/QTLs (Quantitative Trait Loci) associated with reproductive and yield-related traits. The candidate genes/QTLs regulating heat tolerance in the selected landrace E42 could be further investigated to better understand the genetic mechanisms controlling traits for high and stable yield trait under high temperatures

Usability of a Hybrid System Combining P300-Based Brain-Computer Interface and Commercial Assistive Technologies to Enhance Communication in People With Multiple Sclerosis

Brain-computer interface (BCI) can provide people with motor disabilities with an alternative channel to access assistive technology (AT) software for communication and environmental interaction. Multiple sclerosis (MS) is a chronic disease of the central nervous system that mostly starts in young adulthood and often leads to a long-term disability, possibly exacerbated by the presence of fatigue. Patients with MS have been rarely considered as potential BCI end-users. In this pilot study, we evaluated the usability of a hybrid BCI (h-BCI) system that enables both a P300-based BCI and conventional input devices (i.e., muscular dependent) to access mainstream applications through the widely used AT software for communication "Grid 3." The evaluation was performed according to the principles of the user-centered design (UCD) with the aim of providing patients with MS with an alternative control channel (i.e., BCI), potentially less sensitive to fatigue. A total of 13 patients with MS were enrolled. In session I, participants were presented with a widely validated P300-based BCI (P3-speller); in session II, they had to operate Grid 3 to access three mainstream applications with (1) an AT conventional input device and (2) the h-BCI. Eight patients completed the protocol. Five out of eight patients with MS were successfully able to access the Grid 3 via the BCI, with a mean online accuracy of 83.3% (+/- 14.6). Effectiveness (online accuracy), satisfaction, and workload were comparable between the conventional AT inputs and the BCI channel in controlling the Grid 3. As expected, the efficiency (time for correct selection) resulted to be significantly lower for the BCI with respect to the AT conventional channels (Z = 0.2, p < 0.05). Although cautious due to the limited sample size, these preliminary findings indicated that the BCI control channel did not have a detrimental effect with respect to conventional AT channels on the ability to operate an AT software (Grid 3). Therefore, we inferred that the usability of the two access modalities was comparable. The integration of BCI with commercial AT input devices to access a widely used AT software represents an important step toward the introduction of BCIs into the AT centers' daily practice

The Use of a Plant-Based Biostimulant Improves Plant Performances and Fruit Quality in Tomato Plants Grown at Elevated Temperatures.

Abiotic stresses can cause a substantial decline in fruit quality due to negative impacts on plant growth, physiology and reproduction. The objective of this study was to verify if the use of a biostimulant based on plant and yeast extracts, rich in amino acids and that contains microelements (boron, zinc and manganese) can ensure good crop yield and quality in tomato plants grown at elevated temperatures (up to 42 C). We investigated physiological responses of four di↵erent tomato landraces that were cultivated under plastic tunnel and treated with the biostimulant CycoFlow. The application of the biostimulant stimulated growth (plants up to 48.5% taller) and number of fruits (up to 105.3%). In plants treated with the biostimulant, antioxidants contents were higher compared to non-treated plants, both in leaves and in fruits. In particular, the content of ascorbic acid increased after treatments with CycoFlow. For almost all the traits studied, the e↵ect of the biostimulant depended on the genotype it was applied on. Altogether, the use of the biostimulant on tomato plants led to better plant performances at elevated temperatures, that could be attributed also to a stronger antioxidant defence system, and to a better fruit nutritional quality

Toward domotic appliances control through a self-paced P300-based BCI

During recent years there has been a growing interest in Brain Computer Interface (BCI) systems as an alternative means of interaction with the external world for people with severe motor disabilities. The use of the P300 event-related potentials as control feature allows users to choose between various options (letters or icons) requiring a very short calibration phase. The aim of this work is to improve performances and flexibility of P300 based BCIs. An efficient BCI system should be able to understand user's intentions from the ongoing EEG, abstaining from doing a selection when the user is engaged in a different activity, and changing its speed of selection depending on current user's attention level. Our self-paced system addresses all these issues representing an important step beyond the classical synchronous P300 BCI that forces the user in a continuous control task. Experimentation has been performed on 10 healthy volunteers acting on a BCI-controlled domestic environment in order to demonstrate the potential usability of BCI systems in everyday life. Results show that the self-paced BCI increases information transfer rate with respect to the synchronous one, being very robust, at the same time, in avoiding false negatives when the user is not engaged in a control task