311 research outputs found

    Channel Selection and Feature Projection for Cognitive Load Estimation Using Ambulatory EEG

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    We present an ambulatory cognitive state classification system to assess the subject's mental load based on EEG measurements. The ambulatory cognitive state estimator is utilized in the context of a real-time augmented cognition (AugCog) system that aims to enhance the cognitive performance of a human user through computer-mediated assistance based on assessments of cognitive states using physiological signals including, but not limited to, EEG. This paper focuses particularly on the offline channel selection and feature projection phases of the design and aims to present mutual-information-based techniques that use a simple sample estimator for this quantity. Analyses conducted on data collected from 3 subjects performing 2 tasks (n-back/Larson) at 2 difficulty levels (low/high) demonstrate that the proposed mutual-information-based dimensionality reduction scheme can achieve up to 94% cognitive load estimation accuracy

    Physically inspired methods and development of data-driven predictive systems.

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    Traditionally building of predictive models is perceived as a combination of both science and art. Although the designer of a predictive system effectively follows a prescribed procedure, his domain knowledge as well as expertise and intuition in the field of machine learning are often irreplaceable. However, in many practical situations it is possible to build well–performing predictive systems by following a rigorous methodology and offsetting not only the lack of domain knowledge but also partial lack of expertise and intuition, by computational power. The generalised predictive model development cycle discussed in this thesis is an example of such methodology, which despite being computationally expensive, has been successfully applied to real–world problems. The proposed predictive system design cycle is a purely data–driven approach. The quality of data used to build the system is thus of crucial importance. In practice however, the data is rarely perfect. Common problems include missing values, high dimensionality or very limited amount of labelled exemplars. In order to address these issues, this work investigated and exploited inspirations coming from physics. The novel use of well–established physical models in the form of potential fields, has resulted in derivation of a comprehensive Electrostatic Field Classification Framework for supervised and semi–supervised learning from incomplete data. Although the computational power constantly becomes cheaper and more accessible, it is not infinite. Therefore efficient techniques able to exploit finite amount of predictive information content of the data and limit the computational requirements of the resource–hungry predictive system design procedure are very desirable. In designing such techniques this work once again investigated and exploited inspirations coming from physics. By using an analogy with a set of interacting particles and the resulting Information Theoretic Learning framework, the Density Preserving Sampling technique has been derived. This technique acts as a computationally efficient alternative for cross–validation, which fits well within the proposed methodology. All methods derived in this thesis have been thoroughly tested on a number of benchmark datasets. The proposed generalised predictive model design cycle has been successfully applied to two real–world environmental problems, in which a comparative study of Density Preserving Sampling and cross–validation has also been performed confirming great potential of the proposed methods

    Physically inspired methods and development of data-driven predictive systems

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    Traditionally building of predictive models is perceived as a combination of both science and art. Although the designer of a predictive system effectively follows a prescribed procedure, his domain knowledge as well as expertise and intuition in the field of machine learning are often irreplaceable. However, in many practical situations it is possible to build well–performing predictive systems by following a rigorous methodology and offsetting not only the lack of domain knowledge but also partial lack of expertise and intuition, by computational power. The generalised predictive model development cycle discussed in this thesis is an example of such methodology, which despite being computationally expensive, has been successfully applied to real–world problems. The proposed predictive system design cycle is a purely data–driven approach. The quality of data used to build the system is thus of crucial importance. In practice however, the data is rarely perfect. Common problems include missing values, high dimensionality or very limited amount of labelled exemplars. In order to address these issues, this work investigated and exploited inspirations coming from physics. The novel use of well–established physical models in the form of potential fields, has resulted in derivation of a comprehensive Electrostatic Field Classification Framework for supervised and semi–supervised learning from incomplete data. Although the computational power constantly becomes cheaper and more accessible, it is not infinite. Therefore efficient techniques able to exploit finite amount of predictive information content of the data and limit the computational requirements of the resource–hungry predictive system design procedure are very desirable. In designing such techniques this work once again investigated and exploited inspirations coming from physics. By using an analogy with a set of interacting particles and the resulting Information Theoretic Learning framework, the Density Preserving Sampling technique has been derived. This technique acts as a computationally efficient alternative for cross–validation, which fits well within the proposed methodology. All methods derived in this thesis have been thoroughly tested on a number of benchmark datasets. The proposed generalised predictive model design cycle has been successfully applied to two real–world environmental problems, in which a comparative study of Density Preserving Sampling and cross–validation has also been performed confirming great potential of the proposed methods.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

    An information theoretic learning framework based on Renyi’s α entropy for brain effective connectivity estimation

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    The interactions among neural populations distributed across different brain regions are at the core of cognitive and perceptual processing. Therefore, the ability of studying the flow of information within networks of connected neural assemblies is of fundamental importance to understand such processes. In that regard, brain connectivity measures constitute a valuable tool in neuroscience. They allow assessing functional interactions among brain regions through directed or non-directed statistical dependencies estimated from neural time series. Transfer entropy (TE) is one such measure. It is an effective connectivity estimation approach based on information theory concepts and statistical causality premises. It has gained increasing attention in the literature because it can capture purely nonlinear directed interactions, and is model free. That is to say, it does not require an initial hypothesis about the interactions present in the data. These properties make it an especially convenient tool in exploratory analyses. However, like any information-theoretic quantity, TE is defined in terms of probability distributions that in practice need to be estimated from data. A challenging task, whose outcome can significantly affect the results of TE. Also, it lacks a standard spectral representation, so it cannot reveal the local frequency band characteristics of the interactions it detects.Las interacciones entre poblaciones neuronales distribuidas en diferentes regiones del cerebro son el núcleo del procesamiento cognitivo y perceptivo. Por lo tanto, la capacidad de estudiar el flujo de información dentro de redes de conjuntos neuronales conectados es de fundamental importancia para comprender dichos procesos. En ese sentido, las medidas de conectividad cerebral constituyen una valiosa herramienta en neurociencia. Permiten evaluar interacciones funcionales entre regiones cerebrales a través de dependencias estadísticas dirigidas o no dirigidas estimadas a partir de series de tiempo. La transferencia de entropía (TE) es una de esas medidas. Es un enfoque de estimación de conectividad efectiva basada en conceptos de teoría de la información y premisas de causalidad estadística. Ha ganado una atención cada vez mayor en la literatura porque puede capturar interacciones dirigidas puramente no lineales y no depende de un modelo. Es decir, no requiere de una hipótesis inicial sobre las interacciones presentes en los datos. Estas propiedades la convierten en una herramienta especialmente conveniente en análisis exploratorios. Sin embargo, como cualquier concepto basado en teoría de la información, la TE se define en términos de distribuciones de probabilidad que en la práctica deben estimarse a partir de datos. Una tarea desafiante, cuyo resultado puede afectar significativamente los resultados de la TE. Además, carece de una representación espectral estándar, por lo que no puede revelar las características de banda de frecuencia local de las interacciones que detecta.DoctoradoDoctor(a) en IngenieríaContents List of Figures xi List of Tables xv Notation xvi 1 Preliminaries 1 1.1 Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 1.2 Problem statement . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 1.2.1 Probability distribution estimation as an intermediate step in TE computation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 1.2.2 The lack of a spectral representation for TE . . . . . . . . . . . . 7 1.3 Theoretical background . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9 1.3.1 Transfer entropy . . . . . . . . . . . . . . . . . . . . . . . . . . . 9 1.3.2 Granger causality . . . . . . . . . . . . . . . . . . . . . . . . . . . 11 1.3.3 Information theoretic learning from kernel matrices . . . . . . . . 12 1.4 Literature review on transfer entropy estimation . . . . . . . . . . . . . . 14 1.4.1 Transfer entropy in the frequency domain . . . . . . . . . . . . . . 17 1.5 Aims . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22 1.5.1 General aim . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22 1.5.2 Specific aims . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22 1.6 Outline and contributions . . . . . . . . . . . . . . . . . . . . . . . . . . 23 1.6.1 Kernel-based Renyi’s transfer entropy . . . . . . . . . . . . . . . . 24 1.6.2 Kernel-based Renyi’s phase transfer entropy . . . . . . . . . . . . 24 1.6.3 Kernel-based Renyi’s phase transfer entropy for the estimation of directed phase-amplitude interactions . . . . . . . . . . . . . . . . 25 1.7 EEG databases . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26 Contents ix 1.7.1 Motor imagery . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26 1.7.2 Working memory . . . . . . . . . . . . . . . . . . . . . . . . . . . 28 1.8 Thesis structure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31 2 Kernel-based Renyi’s transfer entropy 34 2.1 Kernel-based Renyi’s transfer entropy . . . . . . . . . . . . . . . . . . . . 35 2.2 Experiments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36 2.2.1 VAR model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36 2.2.2 Modified linear Kus model . . . . . . . . . . . . . . . . . . . . . . 38 2.2.3 EEG data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39 2.2.4 Parameter selection . . . . . . . . . . . . . . . . . . . . . . . . . . 42 2.3 Results and discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43 2.3.1 VAR model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43 2.3.2 Modified linear Kus model . . . . . . . . . . . . . . . . . . . . . . 46 2.3.3 EEG data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49 2.3.4 Limitations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57 2.4 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 58 3 Kernel-based Renyi’s phase transfer entropy 60 3.1 Kernel-based Renyi’s phase transfer entropy . . . . . . . . . . . . . . . . 61 3.1.1 Phase-based effective connectivity estimation approaches considered in this chapter . . . . . . . . . . . . . . . . . . . . . . . . . . 61 3.2 Experiments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63 3.2.1 Neural mass models . . . . . . . . . . . . . . . . . . . . . . . . . . 63 3.2.2 EEG data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 66 3.2.3 Parameter selection . . . . . . . . . . . . . . . . . . . . . . . . . . 67 3.3 Results and discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . 68 3.3.1 Neural mass models . . . . . . . . . . . . . . . . . . . . . . . . . . 68 3.3.2 EEG data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 73 3.3.3 Limitations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 82 3.4 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 83 4 Kernel-based Renyi’s phase transfer entropy for the estimation of directed phase-amplitude interactions 84 4.1 Kernel-based Renyi’s phase transfer entropy for the estimation of directed phase-amplitude interactions . . . . . . . . . . . . . . . . . . . . . . . . . 85 x Contents 4.1.1 Transfer entropy for directed phase-amplitude interactions . . . . 85 4.1.2 Cross-frequency directionality . . . . . . . . . . . . . . . . . . . . 85 4.1.3 Phase transfer entropy and directed phase-amplitude interactions 86 4.2 Experiments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 88 4.2.1 Simulated phase-amplitude interactions . . . . . . . . . . . . . . . 88 4.2.2 EEG data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 90 4.2.3 Parameter selection . . . . . . . . . . . . . . . . . . . . . . . . . . 91 4.3 Results and discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . 92 4.3.1 Simulated phase-amplitude interactions . . . . . . . . . . . . . . . 92 4.3.2 EEG data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 94 4.3.3 Limitations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 98 4.4 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 99 5 Final Remarks 100 5.1 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 100 5.2 Future work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 103 5.3 Academic products . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 104 5.3.1 Journal papers . . . . . . . . . . . . . . . . . . . . . . . . . . . . 104 5.3.2 Conference papers . . . . . . . . . . . . . . . . . . . . . . . . . . 105 5.3.3 Conference presentations . . . . . . . . . . . . . . . . . . . . . . . 105 Appendix A Kernel methods and Renyi’s entropy estimation 106 A.1 Reproducing kernel Hilbert spaces . . . . . . . . . . . . . . . . . . . . . . 106 A.1.1 Reproducing kernels . . . . . . . . . . . . . . . . . . . . . . . . . 106 A.1.2 Kernel-based learning . . . . . . . . . . . . . . . . . . . . . . . . . 107 A.2 Kernel-based estimation of Renyi’s entropy . . . . . . . . . . . . . . . . . 109 Appendix B Surface Laplacian 113 Appendix C Permutation testing 115 Appendix D Kernel-based relevance analysis 117 Appendix E Cao’s criterion 120 Appendix F Neural mass model equations 122 References 12

    Classification-Based Adaptive Search Algorithm for Video Motion Estimation

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    A video sequence consists of a series of frames. In order to compress the video for efficient storage and transmission, the temporal redundancy among adjacent frames must be exploited. A frame is selected as reference frame and subsequent frames are predicted from the reference frame using a technique known as motion estimation. Real videos contain a mixture of motions with slow and fast contents. Among block matching motion estimation algorithms, the full search algorithm is known for its superiority in the performance over other matching techniques. However, this method is computationally very extensive. Several fast block matching algorithms (FBMAs) have been proposed in the literature with the aim to reduce computational costs while maintaining desired quality performance, but all these methods are considered to be sub-optimal. No fixed fast block matching algorithm can effi- ciently remove temporal redundancy of video sequences with wide motion contents. Adaptive fast block matching algorithm, called classification based adaptive search (CBAS) has been proposed. A Bayes classifier is applied to classify the motions into slow and fast categories. Accordingly, appropriate search strategy is applied for each class. The algorithm switches between different search patterns according to the content of motions within video frames. The proposed technique outperforms conventional stand-alone fast block matching methods in terms of both peak signal to noise ratio (PSNR) and computational complexity. In addition, a new hierarchical method for detecting and classifying shot boundaries in video sequences is proposed which is based on information theoretic classification (ITC). ITC relies on likelihood of class label transmission of a data point to the data points in its vicinity. ITC focuses on maximizing the global transmission of true class labels and classify the frames into classes of cuts and non-cuts. Applying the same rule, the non-cut frames are also classified into two categories of arbitrary shot frames and gradual transition frames. CBAS is applied on the proposed shot detection method to handle camera or object motions. Experimental evidence demonstrates that our method can detect shot breaks with high accuracy

    Adaptive sequential feature selection in visual perception and pattern recognition

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    In the human visual system, one of the most prominent functions of the extensive feedback from the higher brain areas within and outside of the visual cortex is attentional modulation. The feedback helps the brain to concentrate its resources on visual features that are relevant for recognition, i. e. it iteratively selects certain aspects of the visual scene for refined processing by the lower areas until the inference process in the higher areas converges to a single hypothesis about this scene. In order to minimize a number of required selection-refinement iterations, one has to find a short sequence of maximally informative portions of the visual input. Since the feedback is not static, the selection process is adapted to a scene that should be recognized. To find a scene-specific subset of informative features, the adaptive selection process on every iteration utilizes results of previous processing in order to reduce the remaining uncertainty about the visual scene. This phenomenon inspired us to develop a computational algorithm solving a visual classification task that would incorporate such principle, adaptive feature selection. It is especially interesting because usually feature selection methods are not adaptive as they define a unique set of informative features for a task and use them for classifying all objects. However, an adaptive algorithm selects features that are the most informative for the particular input. Thus, the selection process should be driven by statistics of the environment concerning the current task and the object to be classified. Applied to a classification task, our adaptive feature selection algorithm favors features that maximally reduce the current class uncertainty, which is iteratively updated with values of the previously selected features that are observed on the testing sample. In information-theoretical terms, the selection criterion is the mutual information of a class variable and a feature-candidate conditioned on the already selected features, which take values observed on the current testing sample. Then, the main question investigated in this thesis is whether the proposed adaptive way of selecting features is advantageous over the conventional feature selection and in which situations. Further, we studied whether the proposed adaptive information-theoretical selection scheme, which is a computationally complex algorithm, is utilized by humans while they perform a visual classification task. For this, we constructed a psychophysical experiment where people had to select image parts that as they think are relevant for classification of these images. We present the analysis of behavioral data where we investigate whether human strategies of task-dependent selective attention can be explained by a simple ranker based on the mutual information, a more complex feature selection algorithm based on the conventional static mutual information and the proposed here adaptive feature selector that mimics a mechanism of the iterative hypothesis refinement. Hereby, the main contribution of this work is the adaptive feature selection criterion based on the conditional mutual information. Also it is shown that such adaptive selection strategy is indeed used by people while performing visual classification.:1. Introduction 2. Conventional feature selection 3. Adaptive feature selection 4. Experimental investigations of ACMIFS 5. Information-theoretical strategies of selective attention 6. Discussion Appendix Bibliograph

    A multimodal pattern recognition framework for speaker detection

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    Speaker detection is an important component of a speech-based user interface. Audiovisual speaker detection, speech and speaker recognition or speech synthesis for example find multiple applications in human-computer interaction, multimedia content indexing, biometrics, etc. Generally speaking, any interface which relies on speech for communication requires an estimate of the user's speaking state (i.e. whether or not he/she is speaking to the system) for its reliable functioning. One needs therefore to identify the speaker and discriminate from other users or background noise. A human observer would perform such a task very easily, although this decision results from a complex cognitive process referred to as decision-making. Generally speaking, this process starts with the acquisition by the human being of information about the environment, through each of its five senses. The brain then integrates these multiple information. An amazing property of this multi-sensory integration by the brain, as pointed out by cognitive sciences, is the perception of stimuli of different modalities as originating from a single source, provided they are synchronized in space and time. A speaker is a bimodal source emitting jointly an auditory signal and a visual signal (the motion of the articulators during speech production). The two signals are obviously co-occurring spatio-temporally. This interesting property allows us – as human observers – to discriminate between a speaking mouth and a mouth whose motion is not related with the auditory signal. This dissertation deals with the modelling of such a complex decision-making, using a pattern recognition procedure. A pattern recognition process comprises all the stages of an investigation, from data acquisition to classification and assessment of the results. In the audiovisual speaker detection problem, tackled more specifically in this thesis, the data are acquired using only one microphone and camera. The pattern recognizer integrates and combines these two modalities to perform and is therefore denoted as "multimodal". This multimodal approach is expected to increase the performance of the system. But it also raises many questions such as what should be fused, when in the decision process this fusion should take place, and how is it to be achieved. This thesis provides answers to each of these issues through the proposition of detailed solutions for each step of the classification process. The basic principle is to evaluate the synchrony between the audio and video features extracted from potentially speaking mouths, in order to classify each mouth as speaking or not. This synchrony is evaluated through a mutual information based function. A key to success is the extraction of suitable features. The audiovisual data are then processed through an information theoretic feature extraction framework after having been acquired and represented in a tractable way. This feature extraction framework uses jointly the two modalities in a feature-level fusion scheme. This way, the information originating from the common source is recovered while the independent noise is discarded. This approach is shown to minimize the probability of committing an error on the source estimate. These optimal features are put as inputs of the classifier, defined through a hypothesis testing approach. Using jointly the two modalities, it outputs a single decision about the class label of each candidate mouth region ("speaker" or "non-speaker"). Therefore, the acoustic and visual information are combined at both the feature and the decision levels, so that we can talk about a hybrid fusion method. The hypothesis testing approach gives means for evaluating the performance of the classifier itself but also of the whole pattern recognition system. In particular, the added-value offered by the feature extraction step can be assessed. The framework is applied in a first time with a particular emphasis on the audio modality: the information theoretic feature extraction addresses the optimization of the audio features using jointly the video information. As a result, audio features specific to speech production are produced. The system evaluation framework establishes that putting these features at input of the classifier increases its discrimination power with respect to equivalent non-optimized features. Then the enhancement of the video content is addressed more specifically. The mouth motion is obviously the suitable video representation for handling a task such as speaker detection. However, only an estimate of this motion, the optical flow, can be obtained. This estimation relies on the intensity gradient of the image sequence. Graph theory is used to establish a probabilistic model of the relationships between the audio, the motion and the image intensity gradient, in the particular case of a speaking mouth. The interpretation of this model leads back to the optimization function defined for the information theoretic feature extraction. As a result, a scale-space approach is proposed for estimating the optical flow, where the strength of the smoothness constraint is controlled via a mutual information based criterion involving both the audio and the video information. First results are promising even if more extensive tests should be carried out, in noisy conditions in particular. As a conclusion, this thesis proposes a complete pattern recognition framework dedicated to audiovisual speaker detection and minimizing the probability of misclassifying a mouth as "speaker" or "non-speaker". The importance of fusing the audio and video content as soon as at the feature level is demonstrated through the system evaluation stage included in the pattern recognition process
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