Data-driven multivariate and multiscale methods for brain computer interface

This thesis focuses on the development of data-driven multivariate and multiscale methods for brain computer interface (BCI) systems. The electroencephalogram (EEG), the most convenient means to measure neurophysiological activity due to its noninvasive nature, is mainly considered. The nonlinearity and nonstationarity inherent in EEG and its multichannel recording nature require a new set of data-driven multivariate techniques to estimate more accurately features for enhanced BCI operation. Also, a long term goal is to enable an alternative EEG recording strategy for achieving long-term and portable monitoring. Empirical mode decomposition (EMD) and local mean decomposition (LMD), fully data-driven adaptive tools, are considered to decompose the nonlinear and nonstationary EEG signal into a set of components which are highly localised in time and frequency. It is shown that the complex and multivariate extensions of EMD, which can exploit common oscillatory modes within multivariate (multichannel) data, can be used to accurately estimate and compare the amplitude and phase information among multiple sources, a key for the feature extraction of BCI system. A complex extension of local mean decomposition is also introduced and its operation is illustrated on two channel neuronal spike streams. Common spatial pattern (CSP), a standard feature extraction technique for BCI application, is also extended to complex domain using the augmented complex statistics. Depending on the circularity/noncircularity of a complex signal, one of the complex CSP algorithms can be chosen to produce the best classification performance between two different EEG classes. Using these complex and multivariate algorithms, two cognitive brain studies are investigated for more natural and intuitive design of advanced BCI systems. Firstly, a Yarbus-style auditory selective attention experiment is introduced to measure the user attention to a sound source among a mixture of sound stimuli, which is aimed at improving the usefulness of hearing instruments such as hearing aid. Secondly, emotion experiments elicited by taste and taste recall are examined to determine the pleasure and displeasure of a food for the implementation of affective computing. The separation between two emotional responses is examined using real and complex-valued common spatial pattern methods. Finally, we introduce a novel approach to brain monitoring based on EEG recordings from within the ear canal, embedded on a custom made hearing aid earplug. The new platform promises the possibility of both short- and long-term continuous use for standard brain monitoring and interfacing applications

A novel facial expression recognition method using bi-dimensional EMD based edge detection

Facial expressions provide an important channel of nonverbal communication. Facial recognition techniques detect people’s emotions using their facial expressions and have found applications in technical fields such as Human-Computer-Interaction (HCI) and security monitoring. Technical applications generally require fast processing and decision making. Therefore, it is imperative to develop innovative recognition methods that can detect facial expressions effectively and efficiently. Traditionally, human facial expressions are recognized using standard images. Existing methods of recognition require subjective expertise and high computational costs. This thesis proposes a novel method for facial expression recognition using image edge detection based on Bi-dimensional Empirical Mode Decomposition (BEMD). In this research, a BEMD based edge detection algorithm was developed, a facial expression measurement metric was created, and an intensive database testing was conducted. The success rates of recognition suggest that the proposed method could be a potential alternative to traditional methods for human facial expression recognition with substantially lower computational costs. Furthermore, a possible blind-detection technique was proposed as a result of this research. Initial detection results suggest great potential of the proposed method for blind-detection that may lead to even more efficient techniques for facial expression recognition

Decomposition methods for machine learning with small, incomplete or noisy datasets

In many machine learning applications, measurements are sometimes incomplete or noisy resulting in missing features. In other cases, and for different reasons, the datasets are originally small, and therefore, more data samples are required to derive useful supervised or unsupervised classification methods. Correct handling of incomplete, noisy or small datasets in machine learning is a fundamental and classic challenge. In this article, we provide a unified review of recently proposed methods based on signal decomposition for missing features imputation (data completion), classification of noisy samples and artificial generation of new data samples (data augmentation). We illustrate the application of these signal decomposition methods in diverse selected practical machine learning examples including: brain computer interface, epileptic intracranial electroencephalogram signals classification, face recognition/verification and water networks data analysis. We show that a signal decomposition approach can provide valuable tools to improve machine learning performance with low quality datasets.Fil: Caiafa, César Federico. Provincia de Buenos Aires. Gobernación. Comisión de Investigaciones Científicas. Instituto Argentino de Radioastronomía. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Instituto Argentino de Radioastronomía; ArgentinaFil: Sole Casals, Jordi. Center for Advanced Intelligence; JapónFil: Marti Puig, Pere. University of Catalonia; EspañaFil: Sun, Zhe. RIKEN; JapónFil: Tanaka,Toshihisa. Tokyo University of Agriculture and Technology; Japó

Heterogeneous data fusion for brain psychology applications

This thesis aims to apply Empirical Mode Decomposition (EMD), Multiscale Entropy (MSE), and collaborative adaptive filters for the monitoring of different brain consciousness states. Both block based and online approaches are investigated, and a possible extension to the monitoring and identification of Electromyograph (EMG) states is provided. Firstly, EMD is employed as a multiscale time-frequency data driven tool to decompose a signal into a number of band-limited oscillatory components; its data driven nature makes EMD an ideal candidate for the analysis of nonlinear and non-stationary data. This methodology is further extended to process multichannel real world data, by making use of recent theoretical advances in complex and multivariate EMD. It is shown that this can be used to robustly measure higher order features in multichannel recordings to robustly indicate ‘QBD’. In the next stage, analysis is performed in an information theory setting on multiple scales in time, using MSE. This enables an insight into the complexity of real world recordings. The results of the MSE analysis and the corresponding statistical analysis show a clear difference in MSE between the patients in different brain consciousness states. Finally, an online method for the assessment of the underlying signal nature is studied. This method is based on a collaborative adaptive filtering approach, and is shown to be able to approximately quantify the degree of signal nonlinearity, sparsity, and non-circularity relative to the constituent subfilters. To further illustrate the usefulness of the proposed data driven multiscale signal processing methodology, the final case study considers a human-robot interface based on a multichannel EMG analysis. A preliminary analysis shows that the same methodology as that applied to the analysis of brain cognitive states gives robust and accurate results. The analysis, simulations, and the scope of applications presented suggest great potential of the proposed multiscale data processing framework for feature extraction in multichannel data analysis. Directions for future work include further development of real-time feature map approaches and their use across brain-computer and brain-machine interface applications

Serial-EMD: Fast Empirical Mode Decomposition Method for Multi-dimensional Signals Based on Serialization

Empirical mode decomposition (EMD) has developed into a prominent tool for adaptive, scale-based signal analysis in various fields like robotics, security and biomedical engineering. Since the dramatic increase in amount of data puts forward higher requirements for the capability of real-time signal analysis, it is difficult for existing EMD and its variants to trade off the growth of data dimension and the speed of signal analysis. In order to decompose multi-dimensional signals at a faster speed, we present a novel signal-serialization method (serial-EMD), which concatenates multi-variate or multi-dimensional signals into a one-dimensional signal and uses various one-dimensional EMD algorithms to decompose it. To verify the effects of the proposed method, synthetic multi-variate time series, artificial 2D images with various textures and real-world facial images are tested. Compared with existing multi-EMD algorithms, the decomposition time becomes significantly reduced. In addition, the results of facial recognition with Intrinsic Mode Functions (IMFs) extracted using our method can achieve a higher accuracy than those obtained by existing multi-EMD algorithms, which demonstrates the superior performance of our method in terms of the quality of IMFs. Furthermore, this method can provide a new perspective to optimize the existing EMD algorithms, that is, transforming the structure of the input signal rather than being constrained by developing envelope computation techniques or signal decomposition methods. In summary, the study suggests that the serial-EMD technique is a highly competitive and fast alternative for multi-dimensional signal analysis.Fil: Zhang, Jin. Nankai University; ChinaFil: Feng, Fan. Nankai University; ChinaFil: Marti Puig, Pere. Central University of Catalonia; EspañaFil: Caiafa, César Federico. Provincia de Buenos Aires. Gobernación. Comisión de Investigaciones Científicas. Instituto Argentino de Radioastronomía. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Instituto Argentino de Radioastronomía; ArgentinaFil: Sun, Zhe. RIKEN; JapónFil: Duan, Feng. Nankai University; ChinaFil: Sole Casals, Jordi. Central University of Catalonia; Españ

Using blind image filtering for images from TEM microscopes

Předložená práce se zabývá problematikou slepé filtrace obrazů z transmisního elektronového mikroskopu. V úvodu práce je uveden popis transmisního elektronového mikroskopu. Navazující část popisuje mechanismy interakce elektronů se zkoumaným vzorkem a z toho vyplývající zobrazovací techniky elektronové mikroskopie. Poslední kapitola teoretické části práce zahrnuje popis vybraných metod slepé filtrace obrazu zejména s využitím dekompozice obrazu na charakteristické složky. Taktéž je zde uveden výčet metod pro zhodnocení úspěšnosti filtrace. V praktické části jsou popsány aplikované metody slepé filtrace obrazů a výsledky filtrování. Jednotlivé metody jsou mezi sebou porovnány. Získané výsledky a využitelnost aplikovaných metod jsou zhodnoceny v diskuzi.This work deals with the blind filtration of the images from the transmission electron microscope. At the beginning of this work there is a basic description of the transmission electron microscope. Following part describes the mechanisms of electron interactions with the observed specimen. Description of basic electron microscopy imaging techniques is included. The last chapter of the theoretical part includes the description of several chosen blind image filtration techniques, especially those using the decomposition of the image into characteristic components. It also contains a summary of methods for evaluation the filtration effectiveness. The practical part focuses on a description of applied blind filtering methods and brings the results of the filtration. Individual methods are compared. In conclusion, the obtained results and usability of the applied methods are discussed.

Data-driven time-frequency analysis of multivariate data

Empirical Mode Decomposition (EMD) is a data-driven method for the decomposition and time-frequency analysis of real world nonstationary signals. Its main advantages over other time-frequency methods are its locality, data-driven nature, multiresolution-based decomposition, higher time-frequency resolution and its ability to capture oscillation of any type (nonharmonic signals). These properties have made EMD a viable tool for real world nonstationary data analysis. Recent advances in sensor and data acquisition technologies have brought to light new classes of signals containing typically several data channels. Currently, such signals are almost invariably processed channel-wise, which is suboptimal. It is, therefore, imperative to design multivariate extensions of the existing nonlinear and nonstationary analysis algorithms as they are expected to give more insight into the dynamics and the interdependence between multiple channels of such signals. To this end, this thesis presents multivariate extensions of the empirical mode de- composition algorithm and illustrates their advantages with regards to multivariate non- stationary data analysis. Some important properties of such extensions are also explored, including their ability to exhibit wavelet-like dyadic filter bank structures for white Gaussian noise (WGN), and their capacity to align similar oscillatory modes from multiple data channels. Owing to the generality of the proposed methods, an improved multi- variate EMD-based algorithm is introduced which solves some inherent problems in the original EMD algorithm. Finally, to demonstrate the potential of the proposed methods, simulations on the fusion of multiple real world signals (wind, images and inertial body motion data) support the analysis

BEMDEC: An Adaptive and Robust Methodology for Digital Image Feature Extraction

The intriguing study of feature extraction, and edge detection in particular, has, as a result of the increased use of imagery, drawn even more attention not just from the field of computer science but also from a variety of scientific fields. However, various challenges surrounding the formulation of feature extraction operator, particularly of edges, which is capable of satisfying the necessary properties of low probability of error (i.e., failure of marking true edges), accuracy, and consistent response to a single edge, continue to persist. Moreover, it should be pointed out that most of the work in the area of feature extraction has been focused on improving many of the existing approaches rather than devising or adopting new ones. In the image processing subfield, where the needs constantly change, we must equally change the way we think. In this digital world where the use of images, for variety of purposes, continues to increase, researchers, if they are serious about addressing the aforementioned limitations, must be able to think outside the box and step away from the usual in order to overcome these challenges. In this dissertation, we propose an adaptive and robust, yet simple, digital image features detection methodology using bidimensional empirical mode decomposition (BEMD), a sifting process that decomposes a signal into its two-dimensional (2D) bidimensional intrinsic mode functions (BIMFs). The method is further extended to detect corners and curves, and as such, dubbed as BEMDEC, indicating its ability to detect edges, corners and curves. In addition to the application of BEMD, a unique combination of a flexible envelope estimation algorithm, stopping criteria and boundary adjustment made the realization of this multi-feature detector possible. Further application of two morphological operators of binarization and thinning adds to the quality of the operator

A study of information-theoretic metaheuristics applied to functional neuroimaging datasets

This dissertation presents a new metaheuristic related to a two-dimensional ensemble empirical mode decomposition (2DEEMD). It is based on Green’s functions and is called Green’s Function in Tension - Bidimensional Empirical Mode Decomposition (GiT-BEMD). It is employed for decomposing and extracting hidden information of images. A natural image (face image) as well as images with artificial textures have been used to test and validate the proposed approach. Images are selected to demonstrate efficiency and performance of the GiT-BEMD algorithm in extracting textures on various spatial scales from the different images. In addition, a comparison of the performance of the new algorithm GiT-BEMD with a canonical BEEMD is discussed. Then, GiT-BEMD as well as canonical bidimensional EEMD (BEEMD) are applied to an fMRI study of a contour integration task. Thus, it explores the potential of employing GiT-BEMD to extract such textures, so-called bidimensional intrinsic mode functions (BIMFs), of functional biomedical images. Because of the enormous computational load and the artifacts accompanying the extracted textures when using a canonical BEEMD, GiT-BEMD is developed to cope with such challenges. It is seen that the computational cost is decreased dramatically, and the quality of the extracted textures is enhanced considerably. Consequently, GiT-BEMD achieves a higher quality of the estimated BIMFs as can be seen from a direct comparison of the results obtained with different variants of BEEMD and GiT-BEMD. Moreover, results generated by 2DBEEMD, especially in case of GiT-BEMD, distinctly show a superior precision in spatial localization of activity blobs when compared with a canonical general linear model (GLM) analysis employing statistical parametric mapping (SPM). Furthermore, to identify most informative textures, i.e. BIMFs, a support vector machine (SVM) as well as a random forest (RF) classifier is employed. Classification performance demonstrates the potential of the extracted BIMFs in supporting decision making of the classifier. With GiT-BEMD, the classification performance improved significantly which might also be a consequence of a clearer structure for these modes compared to the ones obtained with canonical BEEMD. Altogether, there is strong believe that the newly proposed metaheuristic GiT-BEMD offers a highly competitive alternative to existing BEMD algorithms and represents a promising technique for blindly decomposing images and extracting textures thereof which may be used for further analysis