Brain decoding: Opportunities and challenges for pattern recognition

The neuroimaging community heavily relies on statistical inference to explain measured brain activity given the experimental paradigm. Undeniably, this method has led to many results, but it is limited by the richness of the generative models that are deployed, typically in a mass-univariate way. Such an approach is suboptimal given the high-dimensional and complex spatiotemporal correlation structure of neuroimaging data

Recent Advances in Signal Processing

The signal processing task is a very critical issue in the majority of new technological inventions and challenges in a variety of applications in both science and engineering fields. Classical signal processing techniques have largely worked with mathematical models that are linear, local, stationary, and Gaussian. They have always favored closed-form tractability over real-world accuracy. These constraints were imposed by the lack of powerful computing tools. During the last few decades, signal processing theories, developments, and applications have matured rapidly and now include tools from many areas of mathematics, computer science, physics, and engineering. This book is targeted primarily toward both students and researchers who want to be exposed to a wide variety of signal processing techniques and algorithms. It includes 27 chapters that can be categorized into five different areas depending on the application at hand. These five categories are ordered to address image processing, speech processing, communication systems, time-series analysis, and educational packages respectively. The book has the advantage of providing a collection of applications that are completely independent and self-contained; thus, the interested reader can choose any chapter and skip to another without losing continuity

Contributions in computational intelligence with results in functional neuroimaging

This thesis applies computational intelligence methodologies to study functional brain images. It is a state-of-the-art application relative to unsupervised learning domain to functional neuroimaging. There are also contributions related to computational intelligence on topics relative to clustering validation and spatio-temporal clustering analysis. Speci_cally, there are the presentation of a new separation measure based on fuzzy sets theory to establish the validity of the fuzzy clustering outcomes and the presentation of a framework to approach the parcellation of functional neuroimages taking in account both spatial and temporal patterns. These contributions have been applied to neuroimages obtained with functional Magnetic Resonance Imaging, using both active and passive paradigm and using both in-house data and fMRI repository. The results obtained shown, globally, an improvement on the quality of the neuroimaging analysis using the methodological contributions proposed

Signal Processing of Electroencephalogram for the Detection of Attentiveness towards Short Training Videos

This research has developed a novel method which uses an easy to deploy single dry electrode wireless electroencephalogram (EEG) collection device as an input to an automated system that measures indicators of a participant’s attentiveness while they are watching a short training video. The results are promising, including 85% or better accuracy in identifying whether a participant is watching a segment of video from a boring scene or lecture, versus a segment of video from an attentiveness inducing active lesson or memory quiz. In addition, the final system produces an ensemble average of attentiveness across many participants, pinpointing areas in the training videos that induce peak attentiveness. Qualitative analysis of the results of this research is also very promising. The system produces attentiveness graphs for individual participants and these triangulate well with the thoughts and feelings those participants had during different parts of the videos, as described in their own words. As distance learning and computer based training become more popular, it is of great interest to measure if students are attentive to recorded lessons and short training videos. This research was motivated by this interest, as well as recent advances in electronic and computer engineering’s use of biometric signal analysis for the detection of affective (emotional) response. Signal processing of EEG has proven useful in measuring alertness, emotional state, and even towards very specific applications such as whether or not participants will recall television commercials days after they have seen them. This research extended these advances by creating an automated system which measures attentiveness towards short training videos. The bulk of the research was focused on electrical and computer engineering, specifically the optimization of signal processing algorithms for this particular application. A review of existing methods of EEG signal processing and feature extraction methods shows that there is a common subdivision of the steps that are used in different EEG applications. These steps include hardware sensing filtering and digitizing, noise removal, chopping the continuous EEG data into windows for processing, normalization, transformation to extract frequency or scale information, treatment of phase or shift information, and additional post-transformation noise reduction techniques. A large degree of variation exists in most of these steps within the currently documented state of the art. This research connected these varied methods into a single holistic model that allows for comparison and selection of optimal algorithms for this application. The research described herein provided for such a structured and orderly comparison of individual signal analysis and feature extraction methods. This study created a concise algorithmic approach in examining all the aforementioned steps. In doing so, the study provided the framework for a systematic approach which followed a rigorous participant cross validation so that options could be tested, compared and optimized. Novel signal analysis methods were also developed, using new techniques to choose parameters, which greatly improved performance. The research also utilizes machine learning to automatically categorize extracted features into measures of attentiveness. The research improved existing machine learning with novel methods, including a method of using per-participant baselines with kNN machine learning. This provided an optimal solution to extend current EEG signal analysis methods that were used in other applications, and refined them for use in the measurement of attentiveness towards short training videos. These algorithms are proven to be best via selection of optimal signal analysis and optimal machine learning steps identified through both n-fold and participant cross validation. The creation of this new system which uses signal processing of EEG for the detection of attentiveness towards short training videos has created a significant advance in the field of attentiveness measuring towards short training videos

Data-driven group analysis of complex-valued fMRI data

Analysis of functional magnetic resonance imaging (fMRI) data in its native, complex form has been shown to increase the sensitivity of the analysis both for data-driven techniques such as independent component analysis (ICA) and for model-driven techniques. The promise of an increase in sensitivity and specificity in clinical studies provides a powerful motivation for utilizing both the phase and magnitude data; however, the unknown and noisy nature of the phase poses a challenge for successful study of the fMRI data. In addition, complex-valued analysis algorithms, such as ICA, suffer from an inherent phase ambiguity, which introduces additional difficulty for group analysis and visualization of the results. We present solutions for these issues, which have been among the main reasons phase information has been traditionally discarded, and show their effectiveness when used as part of a complex-valued group ICA algorithm application. The developed methods become key components of a framework that allows the development of new fully complex data-driven and semi-blind methods to process, analyze, and visualize fMRI data. In this dissertation, we first introduce the methods developed as part of the fully complex framework for ICA of fMRI data. We introduce a physiologically motivated de-noising method that uses phase quality maps to successfully identify and eliminate noisy voxels--3D pixels--in the fMRI complex images so they can be used in individual and group studies. We also introduce a phase correction scheme that can be either applied sub-sequent to ICA of fMRI data or can be incorporated into the ICA algorithm in the form of prior information to eliminate the need for further processing for phase correction. Finally, we present two visualization methods that are used to augment the sensitivity and specificity in the detection of activated voxels. We show the benefits of using the developed methods on actual complex-valued fMRI data. In the remainder of the dissertation, we focus on developing constrained ICA (C-ICA) algorithms for complex-valued fMRI data. C-ICA uses prior information, hence providing a balance between model-based and data-driven approaches such as ICA to improve the source estimation performance and robustness to noise. C-ICA algorithms have been used to improve the estimation performance in real-valued fMRI data, but--to our knowledge--have not been applied to complex-valued fMRI data. We develop the first C-ICA algorithm that uses complex-valued references to constrain either the sources or the mixing coefficients. The designed algorithm is not restricted to having a unitary demixing matrix, which is a major assumption in existing C-ICA algorithms. We show, on both simulated and actual fMRI data, how the performance of ICA improves by using prior information about the fMRI paradigm