A common framework for the evaluation of psychophysiological visual quality assessment

The assessment of perceived quality based on psychophysiological methods recently gained attraction as it potentiallyovercomes certain flaws of psychophysical approaches. Although studies report promising results, it is not possible toarrive at decisive and comparable conclusions that recommend the use of one or another method for a specific applicationor research question. The video quality expert group started a project on psychophysiological quality assessment to studythese novel approaches and to develop a test plan that enables more systematic research. This test plan comprises of a specificallydesigned set of quality annotated video sequences, suggestions for psychophysiological methods to be studied inquality assessment, and recommendations for the documentation and publications of test results. The test plan is presentedin this article.Celtc-Next 5G Perfecta (2018-00735

Comment le sens est-il extrait de l'information visuelle ? Le système visuel exploré des catégories à la conscience

Comment le sens est-il extrait de l'information visuelle ? Cette thèse est focalisée sur la capacité du système visuel d'humains et de singes à extraire et représenter l'information visuelle sur différents niveaux de complexité. Nous avons étudié différent niveaux de représentations visuelles, de la production de représentations visuelles primaires jusqu'à l'élaboration de représentations visuelles conscientes. Ce manuscrit présente six travaux dans lesquels nous avons exploré : (1) les attributs visuels nécessaires pour réaliser la tâche de catégorisation ultra rapide chez l'homme et le singe au moyen de méthodes psychophysiques, (2) la dynamique spatio-temporelle de l'attention visuelle chez l'homme au moyen de méthodes psychophysiques, (3) les corrélats neuronaux des représentations de haut niveau en EEG grâce au développement d'une nouvelle technique appelée SWIFT, (4) les corrélats neuronaux de la conscience visuelle dans la rivalité binoculaire en EEG, (5) la synchronie des signaux cérébraux en fonction de la reconnaissance consciente au moyen d'enregistrements intracrâniens chez des patients épileptiques et (6) les corrélats neuronaux associés à la prise de conscience chez le singe au moyen d'enregistrements intracrâniens. Les résultats de ces travaux nous ont permis d'ébaucher un modèle de la perception visuelle cherchant à dissocier l'attention et la conscience.How does sense emerges in the visual system? In this thesis we will be focused on the visual system of human and non-human primates and their large capacity of extract and represent visual information. We studied several levels of visual representations from those related to the extraction of coarse visual features to the emergence of conscious visual representations. This manuscript presents six works in which we explored: (1) the visual features necessary to perform ultra-rapid visual categorization in monkeys and humans using psychophysics, (2) the spatio-temporal dynamics of visual attention in humans using psychophysics, (3) the neural correlates of high-level visual representations using EEG tanks to the development of an innovative technique called SWIFT, (4) the neural correlates of visual consciousness under binocular rivalry using EEG, (5) the synchrony of brain signals as a function of conscious recognition using intracranial electrodes implanted in epileptic patients and (6) the neural correlates associated with conscious perception in monkeys using intracranial electrodes. The results of these works allowed outlining a tentative model of visual perception aimed to dissociate attention and consciousness

A longitudinal study of cortical EEG to olfactory stimulation : involving inter- and intra- subjective responses

This thesis forms the largest and most systematic study of the topographical EEG response to odour. The evolutionary history of the olfactory sense is briefly presented and its relevance to humans in the present day is considered. This thesis examines the information processing that occurs in this sensory system. The type of processing that the olfactory system utilises at each anatomical stage is discussed. The character of olfactory information that may reach neocortical levels in humans is considered in the light of the technology available to detect such information. The neurogenesis of the EEG is considered, together with questions concerning its postulated functional significance. The empirical work carried out uses the most advanced methodology for this type of study. The large number of odourants and subjects, combined with the longitudinal element, make this the most ambitious study of this nature undertaken. The issues surrounding the analysis and interpretation of EEG data arc fully discussed and the impact of Chaos theory is considered. Five major analysis techniques were used on the data collected, but largely negative findings arc reported. The reasons for the failure of this experimental paradigm are discussed and improvements arc suggested for future work. The major contribution of this thesis lies in its exploration of the assumptions of the EEG response to odour. The thesis notes the lack of a conceptual framework that has hindered progress in the area of the "odour" EEG. Recent developments in neural network theory and Chaos theory are highlighted as possible alternative approaches to the modelling and understanding of the olfactory system

Cortically coupled image computing

In the 1970s, researchers at the University of California started to investigate communication between humans and computers using neural signals, which lead to the emergence of brain- computer interfaces (BCIs). In the past 40 years, significant progress has been achieved in ap- plication areas such as neuroprosthetics and rehabilitation. BCIs have been recently applied to media analytics (e.g., image search and information retrieval) as we are surrounded by tremen- dous amounts of media information today. A cortically coupled computer vision (CCCV) sys- tem is a type of BCI that exposes users to high throughput image streams via the rapid serial visual presentation (RSVP) protocol. Media analytics has also been transformed through the enormous advances in artificial intelligence (AI) in recent times. Understanding and presenting the nature of the human-AI relationship will play an important role in our society in the future. This thesis explores two lines of research in the context of traditional BCIs and AI. Firstly, we study and investigate the fundamental processing methods such as feature extraction and clas- sification for CCCV systems. Secondly, we discuss the feasibility of interfacing neural systems with AI technology through CCCV, an area we identify as neuro-AI interfacing. We have made two electroencephalography (EEG) datasets available to the community that support our inves- tigation of these two research directions. These are the neurally augmented image labelling strategies (NAILS) dataset and the neural indices for face perception analysis (NIFPA) dataset, which are introduced in Chapter 2. The first line of research focuses on studying and investigating fundamental processing methods for CCCV. In Chapter 3, we present a review on recent developments in processing methods for CCCV. This review introduces CCCV related components, specifically the RSVP experimental setup, RSVP-EEG phenomena such as the P300 and N170, evaluation metrics, feature extraction and classification. We then provide a detailed study and an analysis on spatial filtering pipelines in Chapter 4, which are the most widely used feature extraction and reduction methods in a CCCV system. In this context, we propose a spatial filtering technique named multiple time window LDA beamformers (MTWLB) and compare it to two other well-known techniques in the literature, namely xDAWN and common spatial patterns (CSP). Importantly, we demonstrate the efficacy of MTWLB for time-course source signal reconstruction compared to existing methods, which we then use as a source signal information extraction method to support a neuro-AI interface. This will be further discussed in this thesis i.e. Chapter 6 and Chapter 7. The latter part of this thesis investigates the feasibility of neuro-AI interfaces. We present two research studies which contribute to this direction. Firstly, we explore the idea of neuro- AI interfaces based on stimulus and neural systems i.e., observation of the effects of stimuli produced by different AI systems on neural signals. We use generative adversarial networks (GANs) to produce image stimuli in this case as GANs are able to produce higher quality images compared to other deep generative models. Chapter 5 provides a review on GAN-variants in terms of loss functions and architectures. In Chapter 6, we design a comprehensive experiment to verify the effects of images produced by different GANs on participants’ EEG responses. In this we propose a biologically-produced metric called Neuroscore for evaluating GAN per- formance. We highlight the consistency between Neuroscore and human perceptual judgment, which is superior to conventional metrics (i.e., Inception Score (IS), Fre ́chet Inception Distance (FID) and Kernel Maximum Mean Discrepancy (MMD) discussed in this thesis). Secondly, in order to generalize Neuroscore, we explore the use of a neuro-AI interface to help convolutional neural networks (CNNs) predict a Neuroscore with only an image as the input. In this scenario, we feed the reconstructed P300 source signals to the intermediate layer as supervisory informa- tion. We demonstrate that including biological neural information can improve the prediction performance for our proposed CNN models and the predicted Neuroscore is highly correlated with the real Neuroscore (as directly calculated from human neural signals)