Integration of Consonant and Pitch Processing as Revealed by the Absence of Additivity in Mismatch Negativity

Consonants, unlike vowels, are thought to be speech specific and therefore no interactions would be expected between consonants and pitch, a basic element for musical tones. The present study used an electrophysiological approach to investigate whether, contrary to this view, there is integrative processing of consonants and pitch by measuring additivity of changes in the mismatch negativity (MMN) of evoked potentials. The MMN is elicited by discriminable variations occurring in a sequence of repetitive, homogeneous sounds. In the experiment, event-related potentials (ERPs) were recorded while participants heard frequently sung consonant-vowel syllables and rare stimuli deviating in either consonant identity only, pitch only, or in both dimensions. Every type of deviation elicited a reliable MMN. As expected, the two single-deviant MMNs had similar amplitudes, but that of the double-deviant MMN was also not significantly different from them. This absence of additivity in the double-deviant MMN suggests that consonant and pitch variations are processed, at least at a pre-attentive level, in an integrated rather than independent way. Domain-specificity of consonants may depend on higher-level processes in the hierarchy of speech perception

Improvement of image quality of time-domain diffuse optical tomography with lp sparsity regularization

An lp (0 < p ≤ 1) sparsity regularization is applied to time-domain diffuse optical tomography with a gradient-based nonlinear optimization scheme to improve the spatial resolution and the robustness to noise. The expression of the lp sparsity regularization is reformulated as a differentiable function of a parameter to avoid the difficulty in calculating its gradient in the optimization process. The regularization parameter is selected by the L-curve method. Numerical experiments show that the lp sparsity regularization improves the spatial resolution and recovers the difference in the absorption coefficients between two targets, although a target with a small absorption coefficient may disappear due to the strong effect of the lp sparsity regularization when the value of p is too small. The lp sparsity regularization with small p values strongly localizes the target, and the reconstructed region of the target becomes smaller as the value of p decreases. A phantom experiment validates the numerical simulations

Brain source imaging: from sparse to tensor models

International audienceA number of application areas such as biomedical engineering require solving an underdetermined linear inverse problem. In such a case, it is necessary to make assumptions on the sources to restore identifiability. This problem is encountered in brain source imaging when identifying the source signals from noisy electroencephalographic or magnetoencephalographic measurements. This inverse problem has been widely studied during the last decades, giving rise to an impressive number of methods using different priors. Nevertheless, a thorough study of the latter, including especially sparse and tensor-based approaches, is still missing. In this paper, we propose i) a taxonomy of the algorithms based on methodological considerations, ii) a discussion of identifiability and convergence properties, advantages, drawbacks, and application domains of various techniques, and iii) an illustration of the performance of selected methods on identical data sets. Directions for future research in the area of biomedical imaging are eventually provided

Changes in the EEG Spectrum of a Child with Severe Disabilities in Response to Power Mobility Training

Literature suggests that self-generated locomotion in infancy and early childhood enhances the development of various cognitive processes such as spatial awareness, social interaction, language development and differential attentiveness. Thus, having access to a power mobility device may play a crucial role for the overall development, mental health, and quality of life of children with multiple, severe disabilities who have limited motor control. This study investigates the feasibility of using electroencephalography (EEG) as an objective measure to detect changes in brain activity in a child due to power mobility training. EEG data was collected with a modified wireless neuroheadset using a single-subject A-B-A-B design consisting of two baseline phases (A) and two intervention phases (B). One trial consisted of three different activities during baseline phase; resting condition at the beginning (Resting 1) and at the end (Resting 2) of the trial, interaction with adults, and passive mobility. The intervention phase included a forth activity, the use of power mobility, while power mobility training was performed on another day within the same week of data collection. The EEG spectrum between 2.0 and 12.0 Hz was analyzed for Resting 1 and Resting 2 condition in each phase. We found significant increase of theta power and decrease in alpha power during all three phases following the first baseline. In respect of previous findings, these observations may be related to an increase in alertness and/or anticipation. Analysis of the percentage change from Resting 1 to Resting 2 condition revealed decrease in theta and increasing alpha power during the first intervention phase, which could be associated with increasing cognitive capacity immediately after the use of power mobility. Overall, no significant difference between baseline phase and intervention phase was observed. Thus, whether the observed changes may have been influenced or enhanced by power mobility training remains unclear and warrants further investigation

EEG/MEG Sparse Source Imaging and Its Application in Epilepsy

This dissertation is a summary of my Ph.D. work on the development of sparse source imaging technologies based on electroencephalography (EEG) and magneto-encephalography (MEG) and their application to noninvasively reconstruct brain activation from external surface measurements. Conventional sparse source imaging (SSI) methods using the ℓ1-norm regularization to enforce sparseness in the original source domain leads to over-focused solutions and causes bias in estimating spatially extended brain sources. I address the over-focused issue in the ℓ1-norm regularization technique framework by exploring sparseness in the transform domains. First, I apply a SSI method that uses the variation transform, i.e. V-SSI, on clinical MEG interictal recordings from partial epilepsy patients. Estimated epileptic sources by V-SSI are validated using clinical pre-surgical evaluation data and surgical outcomes. Second, I implement a novel face-based wavelet transform, which can efficiently compress brain activation signals into sparse representations on a multi-resolution cortical source model, into the SSI technology framework. The proposed wavelet-based SSI (W-SSI) demonstrates a significantly improved ability in inferring both brain source locations and extents as compared with conventional ℓ2-norm regularizations in obtaining EEG/MEG inverse solutions and other SSI technologies. Furthermore, the face-based wavelet also indicates better performance than a previously reported vertex-based wavelet in W-SSI. I evaluate the W-SSI method and conduct the comparison studies using both simulations and real data collected from partial epilepsy patients. Lastly, I further propose the concept of using multiple transforms in the SSI technology framework and investigated a new SSI method by enforcing sparseness in both variation and face-based wavelet domains, termed as VW-SSI. I conduct simulation studies, which demonstrate that VW-SSI has significantly better detection accuracies in both source locations and extents than conventional ℓ2-norm regularizations and other SSI methods, including SSI, V-SSI, and W-SSI. I further validate the VW-SSI method using clinical MEG data from both language and motor experiments collected from epilepsy patients again to localize their important functional brain areas. The results indicate that VW-SSI provides a performance advantage in detecting neural phenomena that have been extremely difficult to recognize by other EEG/MEG inverse solutions. It thus suggests that the sparse source imaging technique is promising to serve as a non-invasive tool in assisting pre-surgical planning for partial epilepsy patients

Entwicklung von Klassifikatoren zur Analyse und Interpretation zeitvarianter Signale und deren Anwendung auf Biosignale

Die Auswertung von Zeitreihen mit Hilfe von Data-Mining Verfahren ist häufig durch zeitvariante Änderungen der Zeitreihen erschwert. Zur Verbesserung der Ergebnisse bei solchen Datensätzen wird in der vorliegenden Arbeit die gezielte Ausnutzung von zeitlichen Informationen beim Entwurf und der Anwendung von Klassifikatoren für Zeitreihen vorgeschlagen. Durch die neuen Verfahren können die Klassifikatoren nicht nur bessere Ergebnisse erzielen, sondern sind auch robuster gegenüber Störungen