Acoustic Speaker Localization with Strong Reverberation and Adaptive Feature Filtering with a Bayes RFS Framework

The thesis investigates the challenges of speaker localization in presence of strong reverberation, multi-speaker tracking, and multi-feature multi-speaker state filtering, using sound recordings from microphones. Novel reverberation-robust speaker localization algorithms are derived from the signal and room acoustics models. A multi-speaker tracking filter and a multi-feature multi-speaker state filter are developed based upon the generalized labeled multi-Bernoulli random finite set framework. Experiments and comparative studies have verified and demonstrated the benefits of the proposed methods

A Study of Biomedical Time Series Using Empirical Mode Decomposition : Extracting event-related modes from EEG signals recorded during visual processing of contour stimuli

Noninvasive neuroimaging techniques like functional Magnetic Resonance Imaging (fMRI) and/or Electroencephalography (EEG) allow researchers to investigate and analyze brain activities during visual processing. EEG offers a high temporal resolution at a level of submilliseconds which can be combined favorably with fMRI which has a good spatial resolution on small spatial scales in the millimeter range. These neuroimaging techniques were, and still are instrumental in the diagnoses and treatments of neurological disorders in the clinical applications. In this PhD thesis we concentrate on lectrophysiological signatures within EEG recordings of a combined EEG-fMRI data set which where taken while performing a contour integration task. The estimation of location and distribution of the electrical sources in the brain from surface recordings which are responsible for interesting EEG waves has drawn the attention of many EEG/MEG researchers. However, this process which is called brain source localization is still one of the major problems in EEG. It consists of solving two modeling problems: forward and inverse. In the forward problem, one is interested in predicting the expected potential distribution on the scalp from given electrical sources that represent active neurons in the head. These evaluations are necessary to solve the inverse problem which can be defined as the problem of estimating the brain sources that generated the measured electrical potentials. This thesis presents a data-driven analysis of EEG data recorded during a combined EEG/fMRI study of visual processing during a contour integration task. The analysis is based on an ensemble empirical mode decomposition (EEMD) and discusses characteristic features of event related modes (ERMs) resulting from the decomposition. We identify clear differences in certain ERMs in response to contour vs non-contour Gabor stimuli mainly for response amplitudes peaking around 100 [ms] (called P100) and 200 [ms] (called N200) after stimulus onset, respectively. We observe early P100 and N200 responses at electrodes located in the occipital area of the brain, while late P100 and N200 responses appear at electrodes located in frontal brain areas. Signals at electrodes in central brain areas show bimodal early/late response signatures in certain ERMs. Head topographies clearly localize statistically significant response differences to both stimulus conditions. Our findings provide an independent proof of recent models which suggest that contour integration depends on distributed network activity within the brain. Next and based on the previous analysis, a new approach for source localization of EEG data based on combining ERMs, extracted with EEMD, with inverse models has been presented. As the first step, 64 channel EEG recordings are pooled according to six brain areas and decomposed, by applying an EEMD, into their underlying ERMs. Then, based upon the problem at hand, the most closely related ERM, in terms of frequency and amplitude, is combined with inverse modeling techniques for source localization. More specifically, the standardized low resolution brain electromagnetic tomography (sLORETA) procedure is employed in this work. Accuracy and robustness of the results indicate that this approach deems highly promising in source localization techniques for EEG data. Given the results of analyses above, it can be said that EMD is able to extract intrinsic signal modes, ERMs, which contain decisive information about responses to contour and non-contour stimuli. Hence, we introduce a new toolbox, called EMDLAB, which serves the growing interest of the signal processing community in applying EMD as a decomposition technique. EMDLAB can be used to perform, easily and effectively, four common types of EMD: plain EMD, ensemble EMD (EEMD), weighted sliding EMD (wSEMD) and multivariate EMD (MEMD) on the EEG data. The main goal of EMDLAB toolbox is to extract characteristics of either the EEG signal by intrinsic mode functions (IMFs) or ERMs. Since IMFs reflect characteristics of the original EEG signal, ERMs reflect characteristics of ERPs of the original signal. The new toolbox is provided as a plug-in to the well-known EEGLAB which enables it to exploit the advantageous visualization capabilities of EEGLAB as well as statistical data analysis techniques provided there for extracted IMFs and ERMs of the signal