Underdetermined Blind Identification for kk-Sparse Component Analysis using RANSAC-based Orthogonal Subspace Search

Sparse component analysis is very popular in solving underdetermined blind source separation (UBSS) problem. Here, we propose a new underdetermined blind identification (UBI) approach for estimation of the mixing matrix in UBSS. Previous approaches either rely on single dominant component or consider $k \leq m-1$ active sources at each time instant, where $m$ is the number of mixtures, but impose constraint on the level of noise replacing inactive sources. Here, we propose an effective, computationally less complex, and more robust to noise UBI approach to tackle such restrictions when $k = m-1$ based on a two-step scenario: (1) estimating the orthogonal complement subspaces of the overall space and (2) identifying the mixing vectors. For this purpose, an integrated algorithm is presented to solve both steps based on Gram-Schmidt process and random sample consensus method. Experimental results using simulated data show more effectiveness of the proposed method compared with the existing algorithms

Evaluating potential EEG-indicators for auditory attention to speech in realistic environmental noise

The human brain is remarkably capable of perceiving relevant sounds in noisy environments but the underlying inter-play of neurophysiology and acoustics is still being investigated. Cortical processing of these sounds in the brain de-pends on attentional demand. One of the most important issues is how to identify whether a person is paying attentionto the relevant sounds or not. The aim of this study was to explore the potential of single-trial electroencephalography(EEG) indicators to distinguish the cortical representation of three sequential tasks — attentive listening to lecturesin background noise, attentive and inattentive listening to background noise alone. Three types of environmentalnoise, including multi-talker babble, fluctuating traffic and highway sounds were employed as the background duringthe first task and the stimulus during the second and third tasks. 23 healthy volunteers were exposed to these threetasks while 64-channels EEG signals were recorded. Alpha-band spectral characteristics (peak frequency and power)were investigated as potential indicators of attention and cortical inhibition. Furthermore, based on the hypothesisof self-similarity as excitation-inhibition balance, long-range temporal correlation of alpha-band activity was quanti-fied based on detrended fluctuation analysis. Finally, the hypothesis of speech envelope entrainment of brain activitymotivated to estimate the delta absolute power for investigating the attended sound. Considering the participant asa random factor, a linear mixed-effect regression was employed to model the estimated indicators as a function oflistening task, EEG channel cluster, and background noise. Strong significant differences were found that support ourhypotheses that auditory attention to speech can be observed via EEG-indicators

EEG correlates of learning from speech presented in environmental noise

How the human brain retains relevant vocal information while suppressing irrelevant sounds is one of the ongoing challenges in cognitive neuroscience. Knowledge of the underlying mechanisms of this ability can be used to identify whether a person is distracted during listening to a target speech, especially in a learning context. This paper investigates the neural correlates of learning from the speech presented in a noisy environment using an ecologically valid learning context and electroencephalography (EEG). To this end, the following listening tasks were performed while 64-channel EEG signals were recorded: (1) attentive listening to the lectures in background sound, (2) attentive listening to the background sound presented alone, and (3) inattentive listening to the background sound. For the first task, 13 lectures of 5 min in length embedded in different types of realistic background noise were presented to participants who were asked to focus on the lectures. As background noise, multi-talker babble, continuous highway, and fluctuating traffic sounds were used. After the second task, a written exam was taken to quantify the amount of information that participants have acquired and retained from the lectures. In addition to various power spectrum-based EEG features in different frequency bands, the peak frequency and long-range temporal correlations (LRTC) of alpha-band activity were estimated. To reduce these dimensions, a principal component analysis (PCA) was applied to the different listening conditions resulting in the feature combinations that discriminate most between listening conditions and persons. Linear mixed-effect modeling was used to explain the origin of extracted principal components, showing their dependence on listening condition and type of background sound. Following this unsupervised step, a supervised analysis was performed to explain the link between the exam results and the EEG principal component scores using both linear fixed and mixed-effect modeling. Results suggest that the ability to learn from the speech presented in environmental noise can be predicted by the several components over the specific brain regions better than by knowing the background noise type. These components were linked to deterioration in attention, speech envelope following, decreased focusing during listening, cognitive prediction error, and specific inhibition mechanisms

EEG Correlates of Learning From Speech Presented in Environmental Noise

How the human brain retains relevant vocal information while suppressing irrelevant sounds is one of the ongoing challenges in cognitive neuroscience. Knowledge of the underlying mechanisms of this ability can be used to identify whether a person is distracted during listening to a target speech, especially in a learning context. This paper investigates the neural correlates of learning from the speech presented in a noisy environment using an ecologically valid learning context and electroencephalography (EEG). To this end, the following listening tasks were performed while 64-channel EEG signals were recorded: (1) attentive listening to the lectures in background sound, (2) attentive listening to the background sound presented alone, and (3) inattentive listening to the background sound. For the first task, 13 lectures of 5 min in length embedded in different types of realistic background noise were presented to participants who were asked to focus on the lectures. As background noise, multi-talker babble, continuous highway, and fluctuating traffic sounds were used. After the second task, a written exam was taken to quantify the amount of information that participants have acquired and retained from the lectures. In addition to various power spectrum-based EEG features in different frequency bands, the peak frequency and long-range temporal correlations (LRTC) of alpha-band activity were estimated. To reduce these dimensions, a principal component analysis (PCA) was applied to the different listening conditions resulting in the feature combinations that discriminate most between listening conditions and persons. Linear mixed-effect modeling was used to explain the origin of extracted principal components, showing their dependence on listening condition and type of background sound. Following this unsupervised step, a supervised analysis was performed to explain the link between the exam results and the EEG principal component scores using both linear fixed and mixed-effect modeling. Results suggest that the ability to learn from the speech presented in environmental noise can be predicted by the several components over the specific brain regions better than by knowing the background noise type. These components were linked to deterioration in attention, speech envelope following, decreased focusing during listening, cognitive prediction error, and specific inhibition mechanisms

A novel underdetermined source recovery algorithm based on k-sparse component analysis

Sparse component analysis (SCA) is a popular method for addressing underdetermined blind source separation in array signal processing applications. We are motivated by problems that arise in the applications where the sources are densely sparse (i.e. the number of active sources is high and very close to the number of sensors). The separation performance of current underdetermined source recovery (USR) solutions, including the relaxation and greedy families, reduces with decreasing the mixing system dimension and increasing the sparsity level (k). In this paper, we present a k-SCA-based algorithm that is suitable for USR in low-dimensional mixing systems. Assuming the sources is at most (m−1) sparse where m is the number of mixtures; the proposed method is capable of recovering the sources from the mixtures given the mixing matrix using a subspace detection framework. Simulation results show that the proposed algorithm achieves better separation performance in k-SCA conditions compared to state-of-the-art USR algorithms such as basis pursuit, minimizing norm-L1, smoothed L0, focal underdetermined system solver and orthogonal matching pursuit

Exploring neural markers modulated by learning from speech in environmental noise using single-trial EEG

De vaardigheid om te leren uit spraak is cruciaal in onze dagelijkse communicatie als mens. Dit wil zeggen dat men gesproken informatie kan begrijpen en kan onthouden (in het korte termijn geheugen) ondanks de aanwezigheid van niet-gerelateerd omgevingsgeluid of achtergrondgeluid. Het doel van dit proefschrift is om de neurale markers te exploreren die gecontroleerd worden door het leren uit spraak in aanwezigheid van omgevingsgeluid, gebruik makend van een enkele elektro-encefalografie (EEG) opname. Het bestuderen van EEG aangebracht op de hoofdhuid in een dergelijke situatie maakt het mogelijk de onderliggende neurale mechanismen van deze vaardigheid te identificeren. Bovendien kan zo ook het effect van verschillende achtergrondgeluiden en het verschil tussen individuen geëvalueerd worden. Technologie gebaseerd op een dergelijke methode kan gebruikt worden om tijdens een les of verhaal het gevoel van de luisteraar te volgen of om de mate van afleiding te identificeren