LIMO EEG: A Toolbox for hierarchical LInear MOdeling of ElectroEncephaloGraphic data

Magnetic- and electric-evoked brain responses have traditionally been analyzed by comparing the peaks or mean amplitudes of signals from selected channels and averaged across trials. More recently, tools have been developed to investigate single trial response variability (e.g., EEGLAB) and to test differences between averaged evoked responses over the entire scalp and time dimensions (e.g., SPM, Fieldtrip). LIMO EEG is a Matlab toolbox (EEGLAB compatible) to analyse evoked responses over all space and time dimensions, while accounting for single trial variability using a simple hierarchical linear modelling of the data. In addition, LIMO EEG provides robust parametric tests, therefore providing a new and complementary tool in the analysis of neural evoked responses

Functional Bipartite Ranking: a Wavelet-Based Filtering Approach

It is the main goal of this article to address the bipartite ranking issue from the perspective of functional data analysis (FDA). Given a training set of independent realizations of a (possibly sampled) second-order random function with a (locally) smooth autocorrelation structure and to which a binary label is randomly assigned, the objective is to learn a scoring function s with optimal ROC curve. Based on linear/nonlinear wavelet-based approximations, it is shown how to select compact finite dimensional representations of the input curves adaptively, in order to build accurate ranking rules, using recent advances in the ranking problem for multivariate data with binary feedback. Beyond theoretical considerations, the performance of the learning methods for functional bipartite ranking proposed in this paper are illustrated by numerical experiments

Applications of clustering analysis to signal processing problems.

Wing-Keung Sim.Thesis (M.Phil.)--Chinese University of Hong Kong, 1999.Includes bibliographical references (leaves 109-114).Abstracts in English and Chinese.Abstract --- p.2摘要 --- p.3Acknowledgements --- p.4Contents --- p.5List of Figures --- p.8List of Tables --- p.9Introductions --- p.10Chapter 1.1 --- Motivation & Aims --- p.10Chapter 1.2 --- Contributions --- p.11Chapter 1.3 --- Structure of Thesis --- p.11Electrophysiological Spike Discrimination --- p.13Chapter 2.1 --- Introduction --- p.13Chapter 2.2 --- Cellular Physiology --- p.13Chapter 2.2.1 --- Action Potential --- p.13Chapter 2.2.2 --- Recording of Spikes Activities --- p.15Chapter 2.2.3 --- Demultiplexing of Multi-Neuron Recordings --- p.17Chapter 2.3 --- Application of Clustering for Mixed Spikes Train Separation --- p.17Chapter 2.3.1 --- Design Principles for Spike Discrimination Procedures --- p.17Chapter 2.3.2 --- Clustering Analysis --- p.18Chapter 2.3.3 --- Comparison of Clustering Techniques --- p.19Chapter 2.4 --- Literature Review --- p.19Chapter 2.4.1 --- Template Spike Matching --- p.19Chapter 2.4.2 --- Reduced Feature Matching --- p.20Chapter 2.4.3 --- Artificial Neural Networks --- p.21Chapter 2.4.4 --- Hardware Implementation --- p.21Chapter 2.5 --- Summary --- p.22Correlation of Perceived Headphone Sound Quality with Physical Parameters --- p.23Chapter 3.1 --- Introduction --- p.23Chapter 3.2 --- Sound Quality Evaluation --- p.23Chapter 3.3 --- Headphone Characterization --- p.26Chapter 3.3.1 --- Frequency Response --- p.26Chapter 3.3.2 --- Harmonic Distortion --- p.26Chapter 3.3.3 --- Voice-Coil Driver Parameters --- p.27Chapter 3.4 --- Statistical Correlation Measurement --- p.29Chapter 3.4.1 --- Correlation Coefficient --- p.29Chapter 3.4.2 --- t Test for Correlation Coefficients --- p.30Chapter 3.5 --- Summary --- p.31Algorithms --- p.32Chapter 4.1 --- Introduction --- p.32Chapter 4.2 --- Principal Component Analysis --- p.32Chapter 4.2.1 --- Dimensionality Reduction --- p.32Chapter 4.2.2 --- PCA Transformation --- p.33Chapter 4.2.3 --- PCA Implementation --- p.36Chapter 4.3 --- Traditional Clustering Methods --- p.37Chapter 4.3.1 --- Online Template Matching (TM) --- p.37Chapter 4.3.2 --- Online Template Matching Implementation --- p.40Chapter 4.3.3 --- K-Means Clustering --- p.41Chapter 4.3.4 --- K-Means Clustering Implementation --- p.44Chapter 4.4 --- Unsupervised Neural Learning --- p.45Chapter 4.4.1 --- Neural Network Basics --- p.45Chapter 4.4.2 --- Artificial Neural Network Model --- p.46Chapter 4.4.3 --- Simple Competitive Learning (SCL) --- p.47Chapter 4.4.4 --- SCL Implementation --- p.49Chapter 4.4.5 --- Adaptive Resonance Theory Network (ART). --- p.50Chapter 4.4.6 --- ART2 Implementation --- p.53Chapter 4.6 --- Summary --- p.55Experimental Design --- p.57Chapter 5.1 --- Introduction --- p.57Chapter 5.2 --- Electrophysiological Spike Discrimination --- p.57Chapter 5.2.1 --- Experimental Design --- p.57Chapter 5.2.2 --- Extracellular Recordings --- p.58Chapter 5.2.3 --- PCA Feature Extraction --- p.59Chapter 5.2.4 --- Clustering Analysis --- p.59Chapter 5.3 --- Correlation of Headphone Sound Quality with physical Parameters --- p.61Chapter 5.3.1 --- Experimental Design --- p.61Chapter 5.3.2 --- Frequency Response Clustering --- p.62Chapter 5.3.3 --- Additional Parameters Measurement --- p.68Chapter 5.3.4 --- Listening Tests --- p.68Chapter 5.3.5 --- Confirmation Test --- p.69Chapter 5.4 --- Summary --- p.70Results --- p.71Chapter 6.1 --- Introduction --- p.71Chapter 6.2 --- Electrophysiological Spike Discrimination: A Comparison of Methods --- p.71Chapter 6.2.1 --- Clustering Labeled Spike Data --- p.72Chapter 6.2.2 --- Clustering of Unlabeled Data --- p.78Chapter 6.2.3 --- Remarks --- p.84Chapter 6.3 --- Headphone Sound Quality Control --- p.89Chapter 6.3.1 --- Headphones Frequency Response Clustering --- p.89Chapter 6.3.2 --- Listening Tests --- p.90Chapter 6.3.3 --- Correlation with Measured Parameters --- p.90Chapter 6.3.4 --- Confirmation Listening Test --- p.92Chapter 6.4 --- Summary --- p.93Conclusions --- p.97Chapter 7.1 --- Future Work --- p.98Chapter 7.1.1 --- Clustering Analysis --- p.98Chapter 7.1.2 --- Potential Applications of Clustering Analysis --- p.99Chapter 7.2 --- Closing Remarks --- p.100Appendix --- p.101Chapter A.1 --- Tables of Experimental Results: (Spike Discrimination) --- p.101Chapter A.2 --- Tables of Experimental Results: (Headphones Measurement) --- p.104Bibliography --- p.109Publications --- p.11

Contribution to privacy-enhancing tecnologies for machine learning applications

For some time now, big data applications have been enabling revolutionary innovation in every aspect of our daily life by taking advantage of lots of data generated from the interactions of users with technology. Supported by machine learning and unprecedented computation capabilities, different entities are capable of efficiently exploiting such data to obtain significant utility. However, since personal information is involved, these practices raise serious privacy concerns. Although multiple privacy protection mechanisms have been proposed, there are some challenges that need to be addressed for these mechanisms to be adopted in practice, i.e., to be “usable” beyond the privacy guarantee offered. To start, the real impact of privacy protection mechanisms on data utility is not clear, thus an empirical evaluation of such impact is crucial. Moreover, since privacy is commonly obtained through the perturbation of large data sets, usable privacy technologies may require not only preservation of data utility but also efficient algorithms in terms of computation speed. Satisfying both requirements is key to encourage the adoption of privacy initiatives. Although considerable effort has been devoted to design less “destructive” privacy mechanisms, the utility metrics employed may not be appropriate, thus the wellness of such mechanisms would be incorrectly measured. On the other hand, despite the advent of big data, more efficient approaches are not being considered. Not complying with the requirements of current applications may hinder the adoption of privacy technologies. In the first part of this thesis, we address the problem of measuring the effect of k-anonymous microaggregation on the empirical utility of microdata. We quantify utility accordingly as the accuracy of classification models learned from microaggregated data, evaluated over original test data. Our experiments show that the impact of the de facto microaggregation standard on the performance of machine-learning algorithms is often minor for a variety of data sets. Furthermore, experimental evidence suggests that the traditional measure of distortion in the community of microdata anonymization may be inappropriate for evaluating the utility of microaggregated data. Secondly, we address the problem of preserving the empirical utility of data. By transforming the original data records to a different data space, our approach, based on linear discriminant analysis, enables k-anonymous microaggregation to be adapted to the application domain of data. To do this, first, data is rotated (projected) towards the direction of maximum discrimination and, second, scaled in this direction, penalizing distortion across the classification threshold. As a result, data utility is preserved in terms of the accuracy of machine learned models for a number of standardized data sets. Afterwards, we propose a mechanism to reduce the running time for the k-anonymous microaggregation algorithm. This is obtained by simplifying the internal operations of the original algorithm. Through extensive experimentation over multiple data sets, we show that the new algorithm gets significantly faster. Interestingly, this remarkable speedup factor is achieved with no additional loss of data utility.Les aplicacions de big data impulsen actualment una accelerada innovació aprofitant la gran quantitat d’informació generada a partir de les interaccions dels usuaris amb la tecnologia. Així, qualsevol entitat és capaç d'explotar eficientment les dades per obtenir utilitat, emprant aprenentatge automàtic i capacitats de còmput sense precedents. No obstant això, sorgeixen en aquest escenari serioses preocupacions pel que fa a la privacitat dels usuaris ja que hi ha informació personal involucrada. Tot i que s'han proposat diversos mecanismes de protecció, hi ha alguns reptes per a la seva adopció en la pràctica, és a dir perquè es puguin utilitzar. Per començar, l’impacte real d'aquests mecanismes en la utilitat de les dades no esta clar, raó per la qual la seva avaluació empírica és important. A més, considerant que actualment es manegen grans volums de dades, una privacitat usable requereix, no només preservació de la utilitat de les dades, sinó també algoritmes eficients en temes de temps de còmput. És clau satisfer tots dos requeriments per incentivar l’adopció de mesures de privacitat. Malgrat que hi ha diversos esforços per dissenyar mecanismes de privacitat menys "destructius", les mètriques d'utilitat emprades no serien apropiades, de manera que aquests mecanismes de protecció podrien estar sent incorrectament avaluats. D'altra banda, tot i l’adveniment del big data, la investigació existent no s’enfoca molt en millorar la seva eficiència. Lamentablement, si els requisits de les aplicacions actuals no es satisfan, s’obstaculitzarà l'adopció de tecnologies de privacitat. A la primera part d'aquesta tesi abordem el problema de mesurar l'impacte de la microagregació k-Gnónima en la utilitat empírica de microdades. Per això, quantifiquem la utilitat com la precisió de models de classificació obtinguts a partir de les dades microagregades. i avaluats sobre dades de prova originals. Els experiments mostren que l'impacte de l’algoritme de rmicroagregació estàndard en el rendiment d’algoritmes d'aprenentatge automàtic és usualment menor per a una varietat de conjunts de dades avaluats. A més, l’evidència experimental suggereix que la mètrica tradicional de distorsió de les dades seria inapropiada per avaluar la utilitat empírica de dades microagregades. Així també estudiem el problema de preservar la utilitat empírica de les dades a l'ésser anonimitzades. Transformant els registres originaIs de dades en un espai de dades diferent, el nostre enfocament, basat en anàlisi de discriminant lineal, permet que el procés de microagregació k-anònima s'adapti al domini d’aplicació de les dades. Per això, primer, les dades són rotades o projectades en la direcció de màxima discriminació i, segon, escalades en aquesta direcció, penalitzant la distorsió a través del llindar de classificació. Com a resultat, la utilitat de les dades es preserva en termes de la precisió dels models d'aprenentatge automàtic en diversos conjunts de dades. Posteriorment, proposem un mecanisme per reduir el temps d'execució per a la microagregació k-anònima. Això s'aconsegueix simplificant les operacions internes de l'algoritme escollit Mitjançant una extensa experimentació sobre diversos conjunts de dades, vam mostrar que el nou algoritme és bastant més ràpid. Aquesta acceleració s'aconsegueix sense que hi ha pèrdua en la utilitat de les dades. Finalment, en un enfocament més aplicat, es proposa una eina de protecció de privacitat d'individus i organitzacions mitjançant l'anonimització de dades sensibles inclosos en logs de seguretat. Es dissenyen diferents mecanismes d'anonimat per implementar-los en base a la definició d'una política de privacitat, en el context d'un projecte europeu que té per objectiu construir un sistema de seguretat unificat