7 research outputs found

    Review of EEG-based pattern classification frameworks for dyslexia

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    Dyslexia is a disability that causes difficulties in reading and writing despite average intelligence. This hidden disability often goes undetected since dyslexics are normal and healthy in every other way. Electroencephalography (EEG) is one of the upcoming methods being researched for identifying unique brain activation patterns in dyslexics. The aims of this paper are to examine pros and cons of existing EEG-based pattern classification frameworks for dyslexia and recommend optimisations through the findings to assist future research. A critical analysis of the literature is conducted focusing on each framework’s (1) data collection, (2) pre-processing, (3) analysis and (4) classification methods. A wide range of inputs as well as classification approaches has been experimented for the improvement in EEG-based pattern classification frameworks. It was uncovered that incorporating reading- and writing-related tasks to experiments used in data collection may help improve these frameworks instead of using only simple tasks, and those unwanted artefacts caused by body movements in the EEG signals during reading and writing activities could be minimised using artefact subspace reconstruction. Further, support vector machine is identified as a promising classifier to be used in EEG-based pattern classification frameworks for dyslexia

    Identification of EEG signal patterns between adults with dyslexia and normal controls

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    Electroencephalography (EEG) is one of the most useful techniques used to represent behaviours of the brain and helps explore valuable insights through the measurement of brain electrical activity. Hence, it plays a vital role in detecting neurological disorders such as epilepsy. Dyslexia is a hidden learning disability with a neurological origin affecting a significant amount of the world population. Studies show unique brain structures and behaviours in individuals with dyslexia and these variations have become more evident with the use of techniques such as EEG, Functional Magnetic Resonance Imaging (fMRI), Magnetoencephalography (MEG) and Positron Emission Tomography (PET). In this thesis, we are particularly interested in discussing the use of EEG to explore unique brain activities of adults with dyslexia. We attempt to discover unique EEG signal patterns between adults with dyslexia compared to normal controls while performing tasks that are more challenging for individuals with dyslexia. These tasks include real--‐word reading, nonsense--‐ word reading, passage reading, Rapid Automatized Naming (RAN), writing, typing, browsing the web, table interpretation and typing of random numbers. Each participant was instructed to perform these specific tasks while staying seated in front of a computer screen with the EEG headset setup on his or her head. The EEG signals captured during these tasks were examined using a machine learning classification framework, which includes signal preprocessing, frequency sub--‐band decomposition, feature extraction, classification and verification. Cubic Support Vector Machine (CSVM) classifiers were developed for separate brain regions of each specified task in order to determine the optimal brain regions and EEG sensors that produce the most unique EEG signal patterns between the two groups. The research revealed that adults with dyslexia generated unique EEG signal patterns compared to normal controls while performing the specific tasks. One of the vital discoveries of this research was that the nonsense--‐words classifiers produced higher Validation Accuracies (VA) compared to real--‐ words classifiers, confirming difficulties in phonological decoding skills seen in individuals with dyslexia are reflected in the EEG signal patterns, which was detected in the left parieto--‐occipital. It was also uncovered that all three reading tasks showed the same optimal brain region, and RAN which is known to have a relationship to reading also showed optimal performance in an overlapping region, demonstrating the likelihood that the association between reading and RAN reflects in the EEG signal patterns. Finally, we were able to discover brain regions that produced exclusive EEG signal patterns between the two groups that have not been reported before for writing, typing, web browsing, table interpretation and typing of random numbers

    Biomedical Signal and Image Processing

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    Written for senior-level and first year graduate students in biomedical signal and image processing, this book describes fundamental signal and image processing techniques that are used to process biomedical information. The book also discusses application of these techniques in the processing of some of the main biomedical signals and images, such as EEG, ECG, MRI, and CT. New features of this edition include the technical updating of each chapter along with the addition of many more examples, the majority of which are MATLAB based

    Phase entrainment and perceptual cycles in audition and vision

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    Des travaux récents indiquent qu'il existe des différences fondamentales entre les systèmes visuel et auditif: tandis que le premier semble échantillonner le flux d'information en provenance de l'environnement, en passant d'un "instantané" à un autre (créant ainsi des cycles perceptifs), la plupart des expériences destinées à examiner ce phénomène de discrétisation dans le système auditif ont mené à des résultats mitigés. Dans cette thèse, au travers de deux expériences de psychophysique, nous montrons que le sous-échantillonnage de l'information à l'entrée des systèmes perceptifs est en effet plus destructif pour l'audition que pour la vision. Cependant, nous révélons que des cycles perceptifs dans le système auditif pourraient exister à un niveau élevé du traitement de l'information. En outre, nos résultats suggèrent que du fait des fluctuations rapides du flot des sons en provenance de l'environnement, le système auditif tend à avoir son activité alignée sur la structure rythmique de ce flux. En synchronisant la phase des oscillations neuronales, elles-mêmes correspondant à différents états d'excitabilité, le système auditif pourrait optimiser activement le moment d'arrivée de ses "instantanés" et ainsi favoriser le traitement des informations pertinentes par rapport aux événements de moindre importance. Non seulement nos résultats montrent que cet entrainement de la phase des oscillations neuronales a des conséquences importantes sur la façon dont sont perçus deux flux auditifs présentés simultanément ; mais de plus, ils démontrent que l'entraînement de phase par un flux langagier inclut des mécanismes de haut niveau. Dans ce but, nous avons créé des stimuli parole/bruit dans lesquels les fluctuations de l'amplitude et du contenu spectral de la parole ont été enlevés, tout en conservant l'information phonétique et l'intelligibilité. Leur utilisation nous a permis de démontrer, au travers de plusieurs expériences, que le système auditif se synchronise à ces stimuli. Plus précisément, la perception, estimée par la détection d'un clic intégré dans les stimuli parole/bruit, et les oscillations neuronales, mesurées par Electroencéphalographie chez l'humain et à l'aide d'enregistrements intracrâniens dans le cortex auditif chez le singe, suivent la rythmique "de haut niveau" liée à la parole. En résumé, les résultats présentés ici suggèrent que les oscillations neuronales sont un mécanisme important pour la discrétisation des informations en provenance de l'environnement en vue de leur traitement par le cerveau, non seulement dans la vision, mais aussi dans l'audition. Pourtant, il semble exister des différences fondamentales entre les deux systèmes: contrairement au système visuel, il est essentiel pour le système auditif de se synchroniser (par entraînement de phase) à son environnement, avec un échantillonnage du flux des informations vraisemblablement réalisé à un niveau hiérarchique élevé.Recent research indicates fundamental differences between the auditory and visual systems: Whereas the visual system seems to sample its environment, cycling between "snapshots" at discrete moments in time (creating perceptual cycles), most attempts at discovering discrete perception in the auditory system failed. Here, we show in two psychophysical experiments that subsampling the very input to the visual and auditory systems is indeed more disruptive for audition; however, the existence of perceptual cycles in the auditory system is possible if they operate on a relatively high level of auditory processing. Moreover, we suggest that the auditory system, due to the rapidly fluctuating nature of its input, might rely to a particularly strong degree on phase entrainment, the alignment between neural activity and the rhythmic structure of its input: By using the low and high excitability phases of neural oscillations, the auditory system might actively control the timing of its "snapshots" and thereby amplify relevant information whereas irrelevant events are suppressed. Not only do our results suggest that the oscillatory phase has important consequences on how simultaneous auditory inputs are perceived; additionally, we can show that phase entrainment to speech sound does entail an active high-level mechanism. We do so by using specifically constructed speech/noise sounds in which fluctuations in low-level features (amplitude and spectral content) of speech have been removed, but intelligibility and high-level features (including, but not restricted to phonetic information) have been conserved. We demonstrate, in several experiments, that the auditory system can entrain to these stimuli, as both perception (the detection of a click embedded in the speech/noise stimuli) and neural oscillations (measured with electroencephalography, EEG, and in intracranial recordings in primary auditory cortex of the monkey) follow the conserved "high-level" rhythm of speech. Taken together, the results presented here suggest that, not only in vision, but also in audition, neural oscillations are an important tool for the discretization and processing of the brain's input. However, there seem to be fundamental differences between the two systems: In contrast to the visual system, it is critical for the auditory system to adapt (via phase entrainment) to its environment, and input subsampling is done most likely on a hierarchically high level of stimulus processing

    Wavelet entropy differentiations of event related potentials in dyslexia

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