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The analysis and understanding of physiological and brain signals is critical in order to decode user’s behavioral/neural outcome measures in different domain scenarios. Personal Health-Care agents have been proposed recently in order to monitor and acquire reliable data from daily activities to enhance control participants’ wellbeing, and the quality of life of multiple non-neurotypical participants in clinical lab-controlled studies. The inclusion of new wearable devices with increased and more compact memory requirements,and the possibility to include long-size datasets on the cloud and network-based applications agile the implementation of new improved computational health-care agents. These new enhanced agents are able to provide services including real time health-care,medical monitoring, and multiple biological outcome measures-based alarms for medicaldoctor diagnosis. In this dissertation we will focus on multiple Signal Processing (SP), Machine Learning (ML), Saliency Relevance Maps (SRM) techniques and classifiers with the purpose to enhance the Personal Health-care agents in a multimodal clinical environment. Therefore, we propose the evaluation of current state-of-the-art methods to evaluate the incidence of successful hypertension detection, categorical and emotion stimuli decoding using biosignals. To evaluate the performance of ML, SP, and SRM techniques proposed in this study, wedivide this thesis document in two main implementations: 1) Four different initial pipelines where we evaluate the SP, and ML methodologies included here for an enhanced a) Hypertension detection based on Blood-Volume-Pulse signal (BVP) and Photoplethysmography (PPG) wearable sensors, b) Heart-Rate (HR) and Inter-beat-interval (IBI) prediction using light adaptive filtering for physical exercise/real environments, c) Object Category stimuli decoding using EEG features and features subspace transformations, and d) Emotion recognition using EEG features from recognized datasets. And 2) A complete performance and robust SRM evaluation of a neural-based Emotion Decoding/Recognition pipeline using EEG features from Autism Spectrum Disorder (ASD) groups. This pipeline is presented as a novel assistive system for lab-controlled Face Emotion Recognition (FER) intervention ASD subjects. In this pipeline we include a Deep ConvNet asthe Deep classifier to extract the correct neural information and decode emotions successfully.

abstract: Robust and stable decoding of neural signals is imperative for implementing a useful neuroprosthesis capable of carrying out dexterous tasks. A nonhuman primate (NHP) was trained to perform combined flexions of the thumb, index and middle fingers in addition to individual flexions and extensions of the same digits. An array of microelectrodes was implanted in the hand area of the motor cortex of the NHP and used to record action potentials during finger movements. A Support Vector Machine (SVM) was used to classify which finger movement the NHP was making based upon action potential firing rates. The effect of four feature selection techniques, Wilcoxon signed-rank test, Relative Importance, Principal Component Analysis, and Mutual Information Maximization was compared based on SVM classification performance. SVM classification was used to examine the functional parameters of (i) efficacy (ii) endurance to simulated failure and (iii) longevity of classification. The effect of using isolated-neuron and multi-unit firing rates was compared as the feature vector supplied to the SVM. The best classification performance was on post-implantation day 36, when using multi-unit firing rates the worst classification accuracy resulted from features selected with Wilcoxon signed-rank test (51.12 ± 0.65%) and the best classification accuracy resulted from Mutual Information Maximization (93.74 ± 0.32%). On this day when using single-unit firing rates, the classification accuracy from the Wilcoxon signed-rank test was 88.85 ± 0.61 % and Mutual Information Maximization was 95.60 ± 0.52% (degrees of freedom =10, level of chance =10%) / Dissertation/Thesis / Masters Thesis Bioengineering 2015

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京都大学 / 新制・課程博士 / 博士(情報学) / 甲第24932号 / 情博第843号 / 新制||情||141(附属図書館) / 京都大学大学院情報学研究科知能情報学専攻 / (主査)教授 神谷 之康, 教授 西田 眞也, 准教授 吉井 和佳 / 学位規則第4条第1項該当 / Doctor of Informatics / Kyoto University / DFAM

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Résumé : Dans ce projet, un décodage neuronal bayésien est effectué sur le colliculus inférieur du cochon d'Inde. Premièrement, On lit les potentiels évoqués grâce aux électrodes et ensuite on en déduit les potentiels d'actions à l'aide de technique de classification des décharges des neurones. Ensuite, un modèle linéaire généralisé (GLM) est entraîné en associant un stimulus acoustique en même temps que les mesures de potentiel qui sont effectuées. Enfin, nous faisons le décodage neuronal de l'activité des neurones en utilisant une méthode d'estimation statistique par maximum à posteriori afin de reconstituer la représentation spectro-temporelle du signal acoustique qui correspond au stimulus acoustique. Dans ce projet, nous étudions l'impact de différents modèles de codage neuronal ainsi que de différentes représentations spectro-temporelles (qu'elles sont supposé représenter le stimulus acoustique équivalent) sur la précision du décodage bayésien de l'activité neuronale enregistrée par le système auditif central. En fait, le modèle va associer une représentation spectro-temporelle équivalente au stimulus acoustique à partir des mesures faites dans le cerveau. Deux modèles de codage sont comparés: un GLM et un modèle bilinéaire généralisé (GBM), chacun avec trois différentes représentations spectro-temporelles des stimuli d'entrée soit un spectrogramme ainsi que deux représentations bio-inspirées: un banc de filtres gammatones et un spikegramme. Les paramètres des GLM et GBM, soit le champ récepteur spectro-temporel, le filtre post décharge et l'entrée non linéaire (seulement pour le GBM) sont adaptés en utilisant un algorithme d'optimisation par maximum de vraisemblance (ML). Le rapport signal sur bruit entre la représentation reconstruite et la représentation originale est utilisé pour évaluer le décodage, c'est-à-dire la précision de la reconstruction. Nous montrons expérimentalement que la précision de la reconstruction est meilleure avec une représentation par spikegramme qu'avec une représentation par spectrogramme et, en outre, que l'utilisation d'un GBM au lieu d'un GLM augmente la précision de la reconstruction. En fait, nos résultats montrent que le rapport signal à bruit de la reconstruction d'un spikegramme avec le modèle GBM est supérieur de 3.3 dB au rapport signal à bruit de la reconstruction d'un spectrogramme avec le modèle GLM. / Abstract : In this project, Bayesian neural decoding is performed on the neural activity recorded from the inferior colliculus of the guinea pig following the presentation of a vocalization. In particular, we study the impact of different encoding models on the accuracy of reconstruction of different spectro-temporal representations of the input stimulus. First voltages recorded from the inferior colliculus of the guinea pig are read and the spike trains are obtained. Then, we fit an encoding model to the stimulus and associated spike trains. Finally, we do neural decoding on the pairs of stimuli and neural activities using the maximum a posteriori optimization method to obtain the reconstructed spectro-temporal representation of the signal. Two encoding models, a generalized linear model (GLM) and a generalized bilinear model (GBM), are compared along with three different spectro-temporal representations of the input stimuli: a spectrogram and two bio-inspired representations, i.e. a gammatone filter bank (GFB) and a spikegram. The parameters of the GLM and GBM including spectro-temporal receptive field, post spike filter and input non linearity (only for the GBM) are fitted using the maximum likelihood optimization (ML) algorithm. Signal to noise ratios between the reconstructed and original representations are used to evaluate the decoding, or reconstruction accuracy. We experimentally show that the reconstruction accuracy is better with the spikegram representation than with the spectrogram and GFB representation. Furthermore, using a GBM instead of a GLM significantly increases the reconstruction accuracy. In fact, our results show that the spikegram reconstruction accuracy with a GBM fitting yields an SNR that is 3.3 dB better than when using the standard decoding approach of reconstructing a spectrogram with GLM fitting.