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

Protein disorder-to-order transition enhances the nucleosome-binding affinity of H1.

Intrinsically disordered proteins are crucial elements of chromatin heterogenous organization. While disorder in the histone tails enables a large variation of inter-nucleosome arrangements, disorder within the chromatin-binding proteins facilitates promiscuous binding to a wide range of different molecular targets, consistent with structural heterogeneity. Among the partially disordered chromatin-binding proteins, the H1 linker histone influences a myriad of chromatin characteristics including compaction, nucleosome spacing, transcription regulation, and the recruitment of other chromatin regulating proteins. Although it is now established that the long C-terminal domain (CTD) of H1 remains disordered upon nucleosome binding and that such disorder favours chromatin fluidity, the structural behaviour and thereby the role/function of the N-terminal domain (NTD) within chromatin is yet unresolved. On the basis of microsecond-long parallel-tempering metadynamics and temperature-replica exchange atomistic molecular dynamics simulations of different H1 NTD subtypes, we demonstrate that the NTD is completely unstructured in solution but undergoes an important disorder-to-order transition upon nucleosome binding: it forms a helix that enhances its DNA binding ability. Further, we show that the helical propensity of the H1 NTD is subtype-dependent and correlates with the experimentally observed binding affinity of H1 subtypes, suggesting an important functional implication of this disorder-to-order transition

Infrared face recognition: a comprehensive review of methodologies and databases

Automatic face recognition is an area with immense practical potential which includes a wide range of commercial and law enforcement applications. Hence it is unsurprising that it continues to be one of the most active research areas of computer vision. Even after over three decades of intense research, the state-of-the-art in face recognition continues to improve, benefitting from advances in a range of different research fields such as image processing, pattern recognition, computer graphics, and physiology. Systems based on visible spectrum images, the most researched face recognition modality, have reached a significant level of maturity with some practical success. However, they continue to face challenges in the presence of illumination, pose and expression changes, as well as facial disguises, all of which can significantly decrease recognition accuracy. Amongst various approaches which have been proposed in an attempt to overcome these limitations, the use of infrared (IR) imaging has emerged as a particularly promising research direction. This paper presents a comprehensive and timely review of the literature on this subject. Our key contributions are: (i) a summary of the inherent properties of infrared imaging which makes this modality promising in the context of face recognition, (ii) a systematic review of the most influential approaches, with a focus on emerging common trends as well as key differences between alternative methodologies, (iii) a description of the main databases of infrared facial images available to the researcher, and lastly (iv) a discussion of the most promising avenues for future research.Comment: Pattern Recognition, 2014. arXiv admin note: substantial text overlap with arXiv:1306.160

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

Discovering and exploiting hidden pockets at protein interfaces

The number of three-dimensional structures of potential protein targets available in several platforms such as the Protein Data Bank is subjected to a constant increase over the last decades. This observation should be an additional motivation to use structure-based methodologies in drug discovery. In the recent years, different success stories of Structure Based Drug Design approach have been reported. However, it has also been shown that a lack of druggability is one of the major causes of failure in the development of a new compound.The concept of druggability can be used to describe proteins with the capability to bind drug-like compounds. A general consensus suggests that around 10% of the human genome codes for molecular targets that can be considered as druggable. Over the years, the protein druggability was studied with a particular interest to capture structural descriptors in order to develop computational methodologies for druggability assessment. Different computational methods have been published to detect and evaluate potential binding sites at protein surfaces. The majority of methods currently available are designed to assess druggability of a static structure. However it is well known that sometimes a few local rearrangements around the binding site can profoundly influence the affinity of a small molecule to its target. The use of techniques such as molecular dynamics (MD) or Metadynamics could be an interesting way to simulate those variations. The goal of this thesis was to design a new computational approach, called JEDI, for druggability assessment using a combination of empirical descriptors that can be collected ‘on-the-fly’ during MD simulations. JEDI is a grid-based approach able to perform the druggability assessment of a binding site in only a few seconds making it one of the fastest methodologies in the field. Agreement between computed and experimental druggability estimates is comparable to literature alternatives. In addition, the estimator is less sensitive than existing methodologies to small structural rearrangements and gives consistent druggability predictions for similar structures of the same protein. Since the JEDI function is continuous and differentiable, the druggability potential can be used as collective variable to rapidly detect cryptic druggable binding sites in proteins with a variety of MD free energy methods

High Performance Video Stream Analytics System for Object Detection and Classification

Due to the recent advances in cameras, cell phones and camcorders, particularly the resolution at which they can record an image/video, large amounts of data are generated daily. This video data is often so large that manually inspecting it for object detection and classification can be time consuming and error prone, thereby it requires automated analysis to extract useful information and meta-data. The automated analysis from video streams also comes with numerous challenges such as blur content and variation in illumination conditions and poses. We investigate an automated video analytics system in this thesis which takes into account the characteristics from both shallow and deep learning domains. We propose fusion of features from spatial frequency domain to perform highly accurate blur and illumination invariant object classification using deep learning networks. We also propose the tuning of hyper-parameters associated with the deep learning network through a mathematical model. The mathematical model used to support hyper-parameter tuning improved the performance of the proposed system during training. The outcomes of various hyper-parameters on system's performance are compared. The parameters that contribute towards the most optimal performance are selected for the video object classification. The proposed video analytics system has been demonstrated to process a large number of video streams and the underlying infrastructure is able to scale based on the number and size of the video stream(s) being processed. The extensive experimentation on publicly available image and video datasets reveal that the proposed system is significantly more accurate and scalable and can be used as a general purpose video analytics system.N/

Machine Learning Methods with Noisy, Incomplete or Small Datasets

In many machine learning applications, available datasets are sometimes incomplete, noisy or affected by artifacts. In supervised scenarios, it could happen that label information has low quality, which might include unbalanced training sets, noisy labels and other problems. Moreover, in practice, it is very common that available data samples are not enough to derive useful supervised or unsupervised classifiers. All these issues are commonly referred to as the low-quality data problem. This book collects novel contributions on machine learning methods for low-quality datasets, to contribute to the dissemination of new ideas to solve this challenging problem, and to provide clear examples of application in real scenarios

The Joint-Decision Trap: Lessons from German Federalism and European Integration

Compared to early expectations, the process of European integration has resulted in a paradox: frustration without disintegration and resilience without progress. The article attempts to develop an institutional explanation for this paradox by exploring the similarities between joint decision making (‘Politikverflechtung’) in German federalism and decision making in the European Community. In both cases, it is argued, the fact that member governments are directly participating in central decisions, and that there is a de facto requirement of unanimous decisions, will systematically generate sub‐optimal policy outcomes unless a ‘problem‐solving’ (as opposed to a ‘bargaining’) style of decision making can be maintained. In fact, the ‘bargaining’ style has prevailed in both cases. The resulting pathologies of public policy have, however, not resulted either in successful strategies for the further Europeanization of policy responsibilities or in the disintegration of unsatisfactory joint‐decision systems. This ‘joint‐decision trap’ is explained by reference to the utility functions of member governments for whom present institutional arrangements, in spite of their sub‐optimal policy output, seem to represent ‘local optima’ when compared to either greater centralization or disintegration

The contextualization of the gospel of Jesus Christ among Bektashi Albanians

https://place.asburyseminary.edu/ecommonsatsdissertations/1675/thumbnail.jp