Title Promoter Affiliations Abstract "The fundamental neurophysiological organization of auditory intensity perception: Unravelling the role of primary auditory perception, gating and attention." "Miet De Letter" "Department of Rehabilitation Sciences, Department of Speech, Language and Hearing Sciences" "Parkinson’s disease is a neurodegenerative disorder that has been associated with auditory intensity perception deficits. To date, the audiological and neurophysiological background of these perception deficits is unknown. Our group will investigate the functioning of modality-specific gating mechanisms, in relation to primary auditory perception and attention. This proposal comprises fundamental and interdisciplinary research in neuroscience." "Distributed signal processing algorithms for auditory attention detection with chronic EEG sensor networks" "Alexander Bertrand" "Dynamical Systems, Signal Processing and Data Analytics (STADIUS)" "Electroencephalography (EEG) is a non-invasive neuromonitoring technique, with potential use for chronic daily-life monitoring. A variety of miniature EEG devices have been proposed for chronic use, but these typically measure only a few EEG channels over a highly localized area, which is insufficient for many potential chronic-EEG applications. In this project, we aim to overcome this problem by deploying a multitude of such miniature EEG modules onto the scalp, and let them communicate over wireless links in a sensor network-like architecture. To reduce energy consumption to viable levels, we will develop distributed EEG signal processing algorithms where the EEG signals are processed locally at each module, while exchanging compressed data with the other modules. For the algorithm design, we will focus on the use case of EEG-based auditory attention dete ction, which has applications in future neuro-steered hearing prostheses." "Mobile EEG and Tensor Approaches for Auditory Attention Analysis in Real-life" "Sabine Van Huffel" "ESAT - STADIUS, Stadius Centre for Dynamical Systems, Signal Processing and Data Analytics" "In recent years there has been considerable interest in recording neurophysiological information from humans in natural environments. With the emergence of high quality mobile EEG equipment, new EEG applications may be within reach. However, to date, the number of studies using true mobile EEG recordings in natural scenarios is surprisingly limited, which questions the feasibility of recording reliable EEG in out-of-the-lab scenarios. Moreover, the cognitive functioning of humans in real-life scenarios is likely to deviate from artificially created lab environments. With the advent of real-life mobile EEG applications and real-time signal processing, current methods need to be re-evaluated, and new aspects of the EEG acquisition should be addressed. The effects of distractions, changes in cognitive load, physical engagement and subject behavioral variability in real-life scenarios are hypothesized to influence neurophysiological brain responses as described in traditional confined EEG experiments.This thesis seeks to address the feasibility of applying mobile EEG for research grade auditory attention experiments in real-life scenarios. Auditory attention is widely recognized as a very important concept that plays a vital role in the way humans process auditory information. It is inherently related to the user's current environment, making it a very relevant subject of study with mobile EEG outside a lab environment. We evaluated several aspects of EEG recording, analysis and interpretation that are of major importance for the application of mobile EEG. Specifically, we evaluate the response to acoustic stimuli in three-class auditory oddball and auditory attention detection (AAD) in natural speech paradigms. The former relies on event-related potentials (ERP) in the EEG in response to artificial stimuli, i.e. P300, which is one of the most studied potentials in EEG, predominantly for brain-computer-interfaces (BCI). In contrast, AAD is based on tracking cortical EEG responses, in relation to attended natural speech, which holds potential for application in assistive devices such as hearing aids. The usage of regular speech stimuli strengthens the natural character of our experimentation. The first part of this thesis focuses on the signal analysis in three-class auditory oddball paradigms. We introduce the concepts of canonical polyadic decompositions (CPD), and decompositions in multi linear (Lr,Lr,1) terms (LL1) of higher-order EEG data. We demonstrate their effectiveness in decomposing EEG datasets in a data-driven way, to obtain relevant components related to the P300. Additionally, we show that it is possible to eliminate the explicit subject-dependent calibration phase with a tensor-based decomposition (CPD/LL1) augmented with non-subject-specific templates, without sacrificing classification accuracy. This allows for instantaneous classification results that, on average, are similar to those of the subject-specific trained models. These tensor approaches lend themselves for use as data-driven classification methods of EEG that could conceivably lead to faster usage of BCI systems and provide meaningful information of the subject's performance from the mobile EEG in a more natural way.Besides classification, we gained considerable insight with regard to the factors in real-life recordings that influence the neurophysiological responses such as the P300. We evaluate the ERP and single-trial characteristics of a three-class auditory oddball paradigm recorded in outdoor scenarios while pedaling on a fixed bike or cycling around freely. In addition, we also carefully evaluate the trial-specific motion artifacts through independent gyroscope measurements and control for muscle artifacts. This work was the first to successfully examine such aspects simultaneously in one study. Our findings suggest that cognitive paradigms measured in natural real-life scenarios are influenced significantly by increased cognitive load due to being in an unconstrained environment. Furthermore, our study paved the way for other free cycling studies; very recently our results were replicated by others. All in all, these results have strengthened our conviction that the lack of subject response is often the bottleneck in active BCIs and the attentional efforts of the subject need to be carefully evaluated. In the last part we address the conscious attentional efforts in more realistic scenarios. To this end we evaluate mobile EEG recordings at-home for learning in an auditory context. We describe a closed-loop online analysis of AAD applied to natural speech in a cocktail party scenario. In addition, the effects of personalized training via neurofeedback are investigated. We conducted two experiments that took place in an office and home environment. The results prove the feasibility of AAD outside the lab, which is promising for future applications such as in auditory assistive devices. Moreover, the high variability between subjects in physiological responses as recorded with the EEG, highlight the importance of considering EEG training to increase the efficiency of the AAD. Preliminary evidence regarding changes in AAD performance during training was obtained and future studies are needed to examine these effects in more detail. Finally, this work suggests that multiple modalities, e.g. behavioral, physical and neurophysiological, need to be considered when evaluating users' cognitive performance exhaustively in real-life situations. To conclude, even though our investigations have only touched upon a limited section of the wide variety of neurophysiological processes, our results demonstrate the feasibility of truly mobile EEG applications. The prospect of being able to achieve (online) application of the auditory oddball and AAD in out-of-the-lab experiments, serves as a continuous incentive for future research. Furthermore, our results encourage future mobile EEG studies to consider a holistic approach in order to extend, in the best possible way, the current lab-based knowledge of cognitive brain monitoring to real-life scenarios." "Online measurement of auditory attention via electroencephalography (EEG) in individuals and groups of learners" "Bert De Smedt" "Dynamical Systems, Signal Processing and Data Analytics (STADIUS), Parenting and Special Education" "Attention plays a crucial role in our understanding of how students learn and are distracted in the classroom. However, there is little to no research on the auditory component of attention, yet it could help us to improve our understanding of (individual differences in) the learning process. Educational scientists study attention through post-hoc measurements, but these are inaccurate and do not account for fluctuations of attention over time. Biomedical engineers, on the other hand, have made progress in recent years in objectively measuring auditory attention via brain measurements such as electroencephalography (EEG), but these methods are not yet adapted to the complexity of the classroom context. Through a strong interdisciplinary collaboration between biomedical engineers and educational scientists, we aim to develop new EEG-based markers of auditory attention during learning. On the one hand, this should lead to new data-driven EEG-based algorithms that quantify attention over time in groups and individuals. On the other hand, these algorithms will be used to determine profiles of students and investigate effects of (neurofeedback) interventions." "Acoustic beamforming based on auditory attention decoding" "Tom Francart" "Research Group Experimental Oto-rhino-laryngology, Dynamical Systems, Signal Processing and Data Analytics (STADIUS)" "Signal processing algorithms in hearing aids and cochlear implants allow to suppress background noise for improved speech intelligibility for the hearing impaired. By using multiple microphones, beamforming techniques can be applied to filter out sound from a target direction, and to suppress the noise from other directions. Traditional binaural beamforming algorithms for hearing devices often assume that the target talker is known or can be derived from the listener’s look direction. However, this assumption is frequently violated in practice, rendering high distortions and sub-optimal noise suppression. Thankfully, recent advances in electroencephalography (EEG) and its applications to auditory attention decoding have offered a potential solution for tracking the listeners auditory attention in a multi-talker environment. This information can be used to steer the hearing aid's signal processing algorithm into filtering out the unattended sound sources and solely emphasizing the attended speaker. During the current doctoral project, different beamforming techniques coupled with EEG-informed auditory attention detection will be explored for optimizing the noise suppression in hearing aids." "EEG-based Auditory Attention Decoding: Towards Neuro-steered Hearing Devices" "Alexander Bertrand" "Research Group Experimental Oto-rhino-laryngology, ESAT - STADIUS, Stadius Centre for Dynamical Systems, Signal Processing and Data Analytics" "People with hearing impairment often have difficulties to understand speech in noisy environments. This can be partly overcome by the use of noise reduction algorithms in auditory prostheses such as hearing aids or cochlear implants. However, in a multi-speaker scenario, such algorithms do not know which speaker is to be enhanced, and which speaker(s) should be treated as noise. When listening to multiple speakers, neural (cortical) activity has been found to phase-lock with that of the attended speech stream, and decoders can be trained to reconstruct the ‘attended’ speech envelope from recorded brain activity, e.g., through EEG. This allows to design auditory attention detection (AAD) algorithms which allow to  detect which speaker a person is attending to in a multi-speaker scenario. This can be exploited in hearing prostheses, e.g., in the form of an AAD-steered noise reduction algorithm. However, deeper understanding into neural processes behind auditory attention, the influence of acoustic listening conditions, speech intelligibility, and the hearing and cognitive abilities of the listener is necessary to eventually realize real-time (closed-loop) neuro-steered noise reduction algorithms to support hearing prostheses." "Listen very carefully! Online tracking of auditory attention via electroencephalography (EEG) in individuals and groups of learners" "Alexander Bertrand" "Dynamical Systems, Signal Processing and Data Analytics (STADIUS), Parenting and Special Education" "Attention plays a vital role in understanding how students learn and get distracted in classroom situations. Educational scientists study attention via post-hoc assessments (self-reports or cognitive tests), yet these are inaccurate and do not capture fluctuations in attention over time. On the other hand, biomedical engineers have recently made progress in objectively tracking attention to auditory stimuli via brain recordings such as electroencephalography (EEG), but these methods are not yet adapted to the complexity of the classroom context. We propose a strong interdisciplinary collaboration between biomedical engineers and educational neuroscientists, to develop novel EEG-based markers of auditory attention during learning. This will lead to new data-driven EEG-based algorithmic tools that quantify attention over time, both in groups and individuals. These tools will be used to identify student profiles and to investigate the effects of (neurofeedback) interventions." "Towards a multimodal multiple-deficit model of Developmental Dyslexia. Visual and auditory temporal processing in relation to attention and noise exclusion in persons with specific reading disabilities." "Pol Ghesquière" "Parenting and Special Education, Research Group Experimental Oto-rhino-laryngology" "Developmental dyslexia (DD) is one of the most prevalent neurodevelopmental disorders, generally present in 5-7% of the primary school population. Although DD has been studied intensively over the past decades and there is agreement on the fact that it is a neurodevelopmental disorder with a genetic origin, no scientific consensus has been reached yet about the underlying cognitive and biological causes of this condition. Indeed, currently there is no single deficit that seems to explain all the behavioural symptoms of all persons with DD. So, it is important to focus on multiple-deficit model to understand this complex disorder. This doctoral project firstly aims to replicate the findings of the auditory temporal processing deficit theory. Secondly, we want to investigate whether there is also evidence for a visual analogue of this causal theory. Since until now only very few studies have explored auditory and visual processing simultaneously in the same group of persons with DD, we thirdly want to evaluate whether auditory and visual temporal processing deficits are cross-modal or modality specific in our participants with DD. Fourthly, we want to explore whether these deficits could be interpreted as noise exclusion deficits or rather as attention related problems. Doing so will enable us to disentangle the question whether the noise exclusion hypothesis can be seen as an alternative for, an addition to, or a specification of the temporal processing deficit hypothesis." "EEG-based Auditory Attention Decoding for neuro-steered hearing aids to improve speech understanding" "Tom Francart" "Research Group Experimental Oto-rhino-laryngology" "People with hearing impairment often have difficulties understandingspeech in noisy environments, leading to social isolation andgenerally decreased quality of life. Hearing aids and cochlearimplants partly solve this problem through signal processingalgorithms that enhance the target speaker and suppress other noisesources. However, in a scenario where multiple speakers talksimultaneously, a fundamental problem appears: how does analgorithm decide which speaker the listener is actually attending to?This can be solved by decoding the attended speaker from measuredbrain signals (using EEG), and accordingly enhancing the attendedspeaker.To date, such auditory attention decoding (AAD) algorithms have notbeen tested in a closed-loop wherein both AAD and noisesuppression are active. This step is essential for real-life applicationof such systems.We will (1) set the system’s parameters according to its boundaryconditions, (2) investigate speech intelligibility improvements with thesystem, (3) evaluate the system for different target groups (type anddegree of hearing loss, age, ...), and fit it accordingly to individualusers. Finally (4), we will work towards a practically applicablesystem and determine how we can make hearing aids with EEGmeasurementssmall and unobtrusive.Altogether, the goal of this research is to define practical, smart,neuro-steered hearing aids to improve speech intelligibility." "Fast, adaptive and wearable auditory attention decoding: towards practical neuro-steered hearing devices" "Alexander Bertrand" "Research Group Experimental Oto-rhino-laryngology, Dynamical Systems, Signal Processing and Data Analytics (STADIUS)" "Hearing aid users have difficulties to understand speech in noisy environments, which is why hearing aids are equipped with noise reduction algorithms. However, these algorithms often fail in so-called ‘cocktail-party’ scenarios with multiple speakers, because they do not know which speaker the user aims to attend to, and which speaker(s) should be treated as noise. Recent research has shown that it is possible to identify the attended speaker by decoding brain activity of the listener recorded with electroencephalography (EEG). Since this discovery, several research studies have combined such auditory attention decoding (AAD) algorithms with acoustic noise reduction algorithms as a proof of concept towards ‘neuro-steered’ hearing aids. However, a practical realization is not yet within reach due to 3 important roadblocks: (1) Current AAD decoders require more than 10 seconds of EEG data to reliably decode attention, which is too long for practical purposes. (2) Current AAD decoders are not able to adapt to the specific EEG signals of the end-user. (3) AAD experiments are typically conducted with bulky EEG recording devices, which can not be worn in daily life. In this project, we will address these 3 deal-breaking roadblocks by designing an adaptive data-driven AAD algorithm that exploits instantaneous brain lateralization (thereby making it fast enough for practical use), and which is amenable to a distributed realization in a wireless network of wearable EEG sensors."