A STEP TOWARD AN INTELLIGENT AND INTEGRATED COMPUTER-AIDED DESIGN OF APPAREL PRODUCTS

An apparel product (or “apparel”) is a human product. The design of an apparel product (or “apparel design”) should share many features of general product design and be conducted with a high degree of systematics and rationality. However, the current practice of apparel design is relatively more experience-based and ad-hoc than it should be. Besides, computer support to apparel design is quite limited in that there are several software systems available for supporting apparel design but they are isolated. Two reasons may explain this above situation: (1) absence of the ontology of apparel and apparel design, and (2) absence of a systematic and rational apparel design process. Furthermore, apparel is a specialized type of product in that all three inherent requirements (i.e., function, comfort related to ergonomics, and pleasure related to aesthetics) are equally important, especially the latter, which creates positive affects in the human wearer. In general, knowledge of how to design an apparel product for pleasure/affects is missing from the current design. The general motivation for the research conducted in this thesis is to locate and articulate this “missing knowledge” in order to advance design technology including computer-aided design for modern apparel products. The specific objectives of the research presented in this thesis are: (1) development of a model for the ontology of apparel or apparel system so that all basic concepts and their relationships related to the apparel system are captured; (2) development of a systematic design process for apparel that captures all the inherent characteristics of design, namely iteration and open-endedness; and (3) development of a computer-aided system for affective design for apparel, whereby human feeling once described can be computed with the result that an apparel product meets the wearer’s “feeling needs” (functional and ergonomic needs are assumed to be satisfied or not the concern of this thesis). There are several challenges to achieving the foregoing objectives. The first of these is the understanding of ontology for apparel and apparel design, given that there are so many types of apparel and ad-hoc apparel design processes in practice. The second challenge is the generalization and aggregation of the various ad-hoc apparel design processes that exist in practice. Third is the challenge presented by imprecise information and knowledge in the aspect of human’s affect. All three above challenges have been tackled and answered in this thesis. The first challenge is tackled with the tool of data modeling especially semantic-oriented data modeling. The second challenge is tackled with the general design theory such as general design phase theory, axiomatic design theory, and FCBPSS knowledge architecture (F: function, C: context, B: behavior, P: principle, SS: state and structure). The third challenge is tacked with the data mining technique and subjective rating technique. Several contributions are made with this thesis. First is the development of a comprehensive ontology model for apparel and apparel design that provides a basis for computer-aided design and manufacturing of apparel in the future. Second is the development of a general apparel design process model that offers a reference model for any specific apparel design process. Third is the provision of new “data mining” technology for acquiring words in human language that express affects. It should be noted that this technology is domain-independent, and thus it is applicable to any other type of product for affective design. The final contribution is the development of a method for searching apparel design parameters which describe an apparel product meeting a wearer’s required feelings described by “feeling words”. The database of words and the algorithm can be readily incorporated into commercial software for computer aided design of apparel products with the new enabler (i.e., design for affect or feeling)

Applied Cognitive Sciences

Cognitive science is an interdisciplinary field in the study of the mind and intelligence. The term cognition refers to a variety of mental processes, including perception, problem solving, learning, decision making, language use, and emotional experience. The basis of the cognitive sciences is the contribution of philosophy and computing to the study of cognition. Computing is very important in the study of cognition because computer-aided research helps to develop mental processes, and computers are used to test scientific hypotheses about mental organization and functioning. This book provides a platform for reviewing these disciplines and presenting cognitive research as a separate discipline

30th International Conference on Information Modelling and Knowledge Bases

Information modelling is becoming more and more important topic for researchers, designers, and users of information systems. The amount and complexity of information itself, the number of abstraction levels of information, and the size of databases and knowledge bases are continuously growing. Conceptual modelling is one of the sub-areas of information modelling. The aim of this conference is to bring together experts from different areas of computer science and other disciplines, who have a common interest in understanding and solving problems on information modelling and knowledge bases, as well as applying the results of research to practice. We also aim to recognize and study new areas on modelling and knowledge bases to which more attention should be paid. Therefore philosophy and logic, cognitive science, knowledge management, linguistics and management science are relevant areas, too. In the conference, there will be three categories of presentations, i.e. full papers, short papers and position papers

Combining multiple Iris matchers using advanced fusion techniques to enhance Iris matching performance

M.Phil. (Electrical And Electronic Engineering)The enormous increase in technology advancement and the need to secure information e ectively has led to the development and implementation of iris image acquisition technologies for automated iris recognition systems. The iris biometric is gaining popularity and is becoming a reliable and a robust modality for future biometric security. Its wide application can be extended to biometric security areas such as national ID cards, banking systems such as ATM, e-commerce, biometric passports but not applicable in forensic investigations. Iris recognition has gained valuable attention in biometric research due to the uniqueness of its textures and its high recognition rates when employed on high biometric security areas. Identity veri cation for individuals becomes a challenging task when it has to be automated with a high accuracy and robustness against spoo ng attacks and repudiation. Current recognition systems are highly a ected by noise as a result of segmentation failure, and this noise factors increase the biometric error rates such as; the FAR and the FRR. This dissertation reports an investigation of score level fusion methods which can be used to enhance iris matching performance. The fusion methods implemented in this project includes, simple sum rule, weighted sum rule fusion, minimum score and an adaptive weighted sum rule. The proposed approach uses an adaptive fusion which maps feature quality scores with the matcher. The fused scores were generated from four various iris matchers namely; the NHD matcher, the WED matcher, the WHD matcher and the POC matcher. To ensure homogeneity of matching scores before fusion, raw scores were normalized using the tanh-estimators method, because it is e cient and robust against outliers. The results were tested against two publicly available databases; namely, CASIA and UBIRIS using two statistical and biometric system measurements namely the AUC and the EER. The results of these two measures gives the AUC = 99:36% for CASIA left images, the AUC = 99:18% for CASIA right images, the AUC = 99:59% for UBIRIS database and the Equal Error Rate (EER) of 0.041 for CASIA left images, the EER = 0:087 for CASIA right images and with the EER = 0:038 for UBIRIS images

Combining multiple Iris matchers using advanced fusion techniques to enhance Iris matching performance

M.Phil. (Electrical And Electronic Engineering)The enormous increase in technology advancement and the need to secure information e ectively has led to the development and implementation of iris image acquisition technologies for automated iris recognition systems. The iris biometric is gaining popularity and is becoming a reliable and a robust modality for future biometric security. Its wide application can be extended to biometric security areas such as national ID cards, banking systems such as ATM, e-commerce, biometric passports but not applicable in forensic investigations. Iris recognition has gained valuable attention in biometric research due to the uniqueness of its textures and its high recognition rates when employed on high biometric security areas. Identity veri cation for individuals becomes a challenging task when it has to be automated with a high accuracy and robustness against spoo ng attacks and repudiation. Current recognition systems are highly a ected by noise as a result of segmentation failure, and this noise factors increase the biometric error rates such as; the FAR and the FRR. This dissertation reports an investigation of score level fusion methods which can be used to enhance iris matching performance. The fusion methods implemented in this project includes, simple sum rule, weighted sum rule fusion, minimum score and an adaptive weighted sum rule. The proposed approach uses an adaptive fusion which maps feature quality scores with the matcher. The fused scores were generated from four various iris matchers namely; the NHD matcher, the WED matcher, the WHD matcher and the POC matcher. To ensure homogeneity of matching scores before fusion, raw scores were normalized using the tanh-estimators method, because it is e cient and robust against outliers. The results were tested against two publicly available databases; namely, CASIA and UBIRIS using two statistical and biometric system measurements namely the AUC and the EER. The results of these two measures gives the AUC = 99:36% for CASIA left images, the AUC = 99:18% for CASIA right images, the AUC = 99:59% for UBIRIS database and the Equal Error Rate (EER) of 0.041 for CASIA left images, the EER = 0:087 for CASIA right images and with the EER = 0:038 for UBIRIS images

ZATLAB : recognizing gestures for artistic performance interaction

Most artistic performances rely on human gestures, ultimately resulting in an elaborate interaction between the performer and the audience. Humans, even without any kind of formal analysis background in music, dance or gesture are typically able to extract, almost unconsciously, a great amount of relevant information from a gesture. In fact, a gesture contains so much information, why not use it to further enhance a performance? Gestures and expressive communication are intrinsically connected, and being intimately attached to our own daily existence, both have a central position in our (nowadays) technological society. However, the use of technology to understand gestures is still somehow vaguely explored, it has moved beyond its first steps but the way towards systems fully capable of analyzing gestures is still long and difficult (Volpe, 2005). Probably because, if on one hand, the recognition of gestures is somehow a trivial task for humans, on the other hand, the endeavor of translating gestures to the virtual world, with a digital encoding is a difficult and illdefined task. It is necessary to somehow bridge this gap, stimulating a constructive interaction between gestures and technology, culture and science, performance and communication. Opening thus, new and unexplored frontiers in the design of a novel generation of multimodal interactive systems. This work proposes an interactive, real time, gesture recognition framework called the Zatlab System (ZtS). This framework is flexible and extensible. Thus, it is in permanent evolution, keeping up with the different technologies and algorithms that emerge at a fast pace nowadays. The basis of the proposed approach is to partition a temporal stream of captured movement into perceptually motivated descriptive features and transmit them for further processing in Machine Learning algorithms. The framework described will take the view that perception primarily depends on the previous knowledge or learning. Just like humans do, the framework will have to learn gestures and their main features so that later it can identify them. It is however planned to be flexible enough to allow learning gestures on the fly. This dissertation also presents a qualitative and quantitative experimental validation of the framework. The qualitative analysis provides the results concerning the users acceptability of the framework. The quantitative validation provides the results about the gesture recognizing algorithms. The use of Machine Learning algorithms in these tasks allows the achievement of final results that compare or outperform typical and state-of-the-art systems. In addition, there are also presented two artistic implementations of the framework, thus assessing its usability amongst the artistic performance domain. Although a specific implementation of the proposed framework is presented in this dissertation and made available as open source software, the proposed approach is flexible enough to be used in other case scenarios, paving the way to applications that can benefit not only the performative arts domain, but also, probably in the near future, helping other types of communication, such as the gestural sign language for the hearing impaired.Grande parte das apresentações artísticas são baseadas em gestos humanos, ultimamente resultando numa intricada interação entre o performer e o público. Os seres humanos, mesmo sem qualquer tipo de formação em música, dança ou gesto são capazes de extrair, quase inconscientemente, uma grande quantidade de informações relevantes a partir de um gesto. Na verdade, um gesto contém imensa informação, porque não usá-la para enriquecer ainda mais uma performance? Os gestos e a comunicação expressiva estão intrinsecamente ligados e estando ambos intimamente ligados à nossa própria existência quotidiana, têm uma posicão central nesta sociedade tecnológica actual. No entanto, o uso da tecnologia para entender o gesto está ainda, de alguma forma, vagamente explorado. Existem já alguns desenvolvimentos, mas o objetivo de sistemas totalmente capazes de analisar os gestos ainda está longe (Volpe, 2005). Provavelmente porque, se por um lado, o reconhecimento de gestos é de certo modo uma tarefa trivial para os seres humanos, por outro lado, o esforço de traduzir os gestos para o mundo virtual, com uma codificação digital é uma tarefa difícil e ainda mal definida. É necessário preencher esta lacuna de alguma forma, estimulando uma interação construtiva entre gestos e tecnologia, cultura e ciência, desempenho e comunicação. Abrindo assim, novas e inexploradas fronteiras na concepção de uma nova geração de sistemas interativos multimodais . Este trabalho propõe uma framework interativa de reconhecimento de gestos, em tempo real, chamada Sistema Zatlab (ZtS). Esta framework é flexível e extensível. Assim, está em permanente evolução, mantendo-se a par das diferentes tecnologias e algoritmos que surgem num ritmo acelerado hoje em dia. A abordagem proposta baseia-se em dividir a sequência temporal do movimento humano nas suas características descritivas e transmiti-las para posterior processamento, em algoritmos de Machine Learning. A framework descrita baseia-se no facto de que a percepção depende, principalmente, do conhecimento ou aprendizagem prévia. Assim, tal como os humanos, a framework terá que aprender os gestos e as suas principais características para que depois possa identificá-los. No entanto, esta está prevista para ser flexível o suficiente de forma a permitir a aprendizagem de gestos de forma dinâmica. Esta dissertação apresenta também uma validação experimental qualitativa e quantitativa da framework. A análise qualitativa fornece os resultados referentes à aceitabilidade da framework. A validação quantitativa fornece os resultados sobre os algoritmos de reconhecimento de gestos. O uso de algoritmos de Machine Learning no reconhecimento de gestos, permite a obtençãoc¸ ˜ao de resultados finais que s˜ao comparaveis ou superam outras implementac¸ ˜oes do mesmo g´enero. Al ´em disso, s˜ao tamb´em apresentadas duas implementac¸ ˜oes art´ısticas da framework, avaliando assim a sua usabilidade no dom´ınio da performance art´ıstica. Apesar duma implementac¸ ˜ao espec´ıfica da framework ser apresentada nesta dissertac¸ ˜ao e disponibilizada como software open-source, a abordagem proposta ´e suficientemente flex´ıvel para que esta seja usada noutros cen´ arios. Abrindo assim, o caminho para aplicac¸ ˜oes que poder˜ao beneficiar n˜ao s´o o dom´ınio das artes performativas, mas tamb´em, provavelmente num futuro pr ´oximo, outros tipos de comunicac¸ ˜ao, como por exemplo, a linguagem gestual usada em casos de deficiˆencia auditiva

Inter-modality image synthesis and recognition.

跨模態圖像的合成和識別已成為計算機視覺領域的熱點。實際應用中存在各種各樣的圖像模態，比如刑偵中使用的素描畫和光照不變人臉識別中使用的近紅外圖像。由於某些模態的圖像很難獲得，模態間的轉換和匹配是一項十分有用的技術，為計算機視覺的應用提供了很大的便利。本論文研究了三個應用：人像素描畫的合成，基於樣本的圖像風格化和人像素描畫識別。我們將人像素描畫的合成的前沿研究擴展到非可控條件下的合成。以前的工作都只能在嚴格可控的條件下從照片合成素描畫。我們提出了一種魯棒的算法，可以從有光照和姿態變化的人臉照片合成素描畫。該算法用多尺度馬爾可夫隨機場來合成局部素描圖像塊。對光照和姿態的魯棒性通過三個部分來實現：基於面部器官的形狀先驗可以抑制缺陷和扭曲的合成效果，圖像塊的特征描述子和魯棒的距離測度用來選擇素描圖像塊，以及像素灰度和梯度的一致性來有效地匹配鄰近的素描圖像塊。在CUHK人像素描數據庫和網上的名人照片上的實驗結果表明我們的算法顯著提高了現有算法的效果。針對基於樣本的圖像風格化，我們提供了一種將模板圖像的藝術風格傳遞到照片上的有效方法。大多數已有方法沒有考慮圖像內容和風格的分離。我們提出了一種通過頻段分解的風格傳遞算法。一幅圖像被分解成低頻、中頻和高頻分量，分別描述內容、主要風格和邊緣信息。接著中頻和高頻分量中的風格從模板傳遞到照片，這一過程用馬爾可夫隨機場來建模。最後我們結合照片中的低頻分量和獲得的風格信息重建出藝術圖像。和其它算法相比，我們的方法不僅合成了風格，而且很好的保持了原有的圖像內容。我們通過圖像風格化和個性化藝術合成的實驗來驗證了算法的有效性。我們為人像素描畫的識別提出了一個從數據中學習人臉描述子的新方向。最近的研究都集中在轉換照片和素描畫到相同的模態，或者設計復雜的分類算法來減少從照片和素描畫提取的特征的模態間差異。我們提出了一種新穎的方法：在提取特征的階段減小模態間差異。我們用一種基於耦合信息論編碼的人臉描述子來獲取有判別性的局部人臉結構和有效的匹配照片和素描畫。通過最大化在量化特征空間的照片和素描畫的互信息，我們設計了耦合信息論投影森林來實現耦合編碼。在世界上最大的人像素描畫數據庫上的結果表明我們的方法和已有最好的方法相比有顯著提高。Inter-modality image synthesis and recognition has been a hot topic in computer vision. In real-world applications, there are diverse image modalities, such as sketch images for law enforcement and near infrared images for illumination invariant face recognition. Therefore, it is often useful to transform images from a modality to another or match images from different modalities, due to the difficulty of acquiring image data in some modality. These techniques provide large flexibility for computer vision applications.In this thesis we study three problems: face sketch synthesis, example-based image stylization, and face sketch recognition.For face sketch synthesis, we expand the frontier to synthesis from uncontrolled face photos. Previous methods only work under well controlled conditions. We propose a robust algorithm for synthesizing a face sketch from a face photo with lighting and pose variations. It synthesizes local sketch patches using a multiscale Markov Random Field (MRF) model. The robustness to lighting and pose variations is achieved with three components: shape priors specific to facial components to reduce artifacts and distortions, patch descriptors and robust metrics for selecting sketch patch candidates, and intensity compatibility and gradient compatibility to match neighboring sketch patches effectively. Experiments on the CUHK face sketch database and celebrity photos collected from the web show that our algorithm significantly improves the performance of the state-of-the-art.For example-based image stylization, we provide an effective approach of transferring artistic effects from a template image to photos. Most existing methods do not consider the content and style separately. We propose a style transfer algorithm via frequency band decomposition. An image is decomposed into the low-frequency (LF), mid-frequency (MF), and highfrequency( HF) components, which describe the content, main style, and information along the boundaries. Then the style is transferred from the template to the photo in the MF and HF components, which is formulated as MRF optimization. Finally a reconstruction step combines the LF component of the photo and the obtained style information to generate the artistic result. Compared to the other algorithms, our method not only synthesizes the style, but also preserves the image content well. We demonstrate that our approach performs excellently in image stylization and personalized artwork in experiments.For face sketch recognition, we propose a new direction based on learning face descriptors from data. Recent research has focused on transforming photos and sketches into the same modality for matching or developing advanced classification algorithms to reduce the modality gap between features extracted from photos and sketches. We propose a novel approach by reducing the modality gap at the feature extraction stage. A face descriptor based on coupled information-theoretic encoding is used to capture discriminative local face structures and to effectively match photos and sketches. Guided by maximizing the mutual information between photos and sketches in the quantized feature spaces, the coupled encoding is achieved by the proposed coupled information-theoretic projection forest. Experiments on the largest face sketch database show that our approach significantly outperforms the state-of-the-art methods.Detailed summary in vernacular field only.Detailed summary in vernacular field only.Detailed summary in vernacular field only.Detailed summary in vernacular field only.Detailed summary in vernacular field only.Zhang, Wei.Thesis (Ph.D.)--Chinese University of Hong Kong, 2012.Includes bibliographical references (leaves 121-137).Abstract also in Chinese.Abstract --- p.iAcknowledgement --- p.vChapter 1 --- Introduction --- p.1Chapter 1.1 --- Multi-Modality Computer Vision --- p.1Chapter 1.2 --- Face Sketches --- p.4Chapter 1.2.1 --- Face Sketch Synthesis --- p.6Chapter 1.2.2 --- Face Sketch Recognition --- p.7Chapter 1.3 --- Example-based Image Stylization --- p.9Chapter 1.4 --- Contributions and Summary of Approaches --- p.10Chapter 1.5 --- Thesis Road Map --- p.13Chapter 2 --- Literature Review --- p.14Chapter 2.1 --- Related Works in Face Sketch Synthesis --- p.14Chapter 2.2 --- Related Works in Example-based Image Stylization --- p.17Chapter 2.3 --- Related Works in Face Sketch Recognition --- p.21Chapter 3 --- Lighting and Pose Robust Sketch Synthesis --- p.27Chapter 3.1 --- The Algorithm --- p.31Chapter 3.1.1 --- Overview of the Method --- p.32Chapter 3.1.2 --- Local Evidence --- p.34Chapter 3.1.3 --- Shape Prior --- p.40Chapter 3.1.4 --- Neighboring Compatibility --- p.42Chapter 3.1.5 --- Implementation Details --- p.43Chapter 3.1.6 --- Acceleration --- p.45Chapter 3.2 --- Experimental Results --- p.47Chapter 3.2.1 --- Lighting and Pose Variations --- p.49Chapter 3.2.2 --- Celebrity Faces from the Web --- p.54Chapter 3.3 --- Conclusion --- p.54Chapter 4 --- Style Transfer via Band Decomposition --- p.58Chapter 4.1 --- Introduction --- p.58Chapter 4.2 --- Algorithm Overview --- p.63Chapter 4.3 --- Image Style Transfer --- p.64Chapter 4.3.1 --- Band Decomposition --- p.64Chapter 4.3.2 --- MF and HF Component Processing --- p.67Chapter 4.3.3 --- Reconstruction --- p.74Chapter 4.4 --- Experiments --- p.76Chapter 4.4.1 --- Comparison to State-of-the-Art --- p.76Chapter 4.4.2 --- Extended Application: Personalized Artwork --- p.82Chapter 4.5 --- Conclusion --- p.84Chapter 5 --- Coupled Encoding for Sketch Recognition --- p.86Chapter 5.1 --- Introduction --- p.86Chapter 5.1.1 --- Related work --- p.89Chapter 5.2 --- Information-Theoretic Projection Tree --- p.90Chapter 5.2.1 --- Projection Tree --- p.91Chapter 5.2.2 --- Mutual Information Maximization --- p.92Chapter 5.2.3 --- Tree Construction with MMI --- p.94Chapter 5.2.4 --- Randomized CITP Forest --- p.102Chapter 5.3 --- Coupled Encoding Based Descriptor --- p.103Chapter 5.4 --- Experiments --- p.106Chapter 5.4.1 --- Descriptor Comparison --- p.108Chapter 5.4.2 --- Parameter Exploration --- p.109Chapter 5.4.3 --- Experiments on Benchmarks --- p.112Chapter 5.5 --- Conclusions --- p.115Chapter 6 --- Conclusion --- p.116Bibliography --- p.12

Natural Language Processing: Emerging Neural Approaches and Applications

This Special Issue highlights the most recent research being carried out in the NLP field to discuss relative open issues, with a particular focus on both emerging approaches for language learning, understanding, production, and grounding interactively or autonomously from data in cognitive and neural systems, as well as on their potential or real applications in different domains

Medical Image Classification using Deep Learning Techniques and Uncertainty Quantification

The emergence of medical image analysis using deep learning techniques has introduced multiple challenges in terms of developing robust and trustworthy systems for automated grading and diagnosis. Several works have been presented to improve classification performance. However, these methods lack the diversity of capturing different levels of contextual information among image regions, strategies to present diversity in learning by using ensemble-based techniques, or uncertainty measures for predictions generated from automated systems. Consequently, the presented methods provide sub-optimal results which is not enough for clinical practice. To enhance classification performance and introduce trustworthiness, deep learning techniques and uncertainty quantification methods are required to provide diversity in contextual learning and the initial stage of explainability, respectively. This thesis aims to explore and develop novel deep learning techniques escorted by uncertainty quantification for developing actionable automated grading and diagnosis systems. More specifically, the thesis provides the following three main contributions. First, it introduces a novel entropy-based elastic ensemble of Deep Convolutional Neural Networks (DCNNs) architecture termed as 3E-Net for classifying grades of invasive breast carcinoma microscopic images. 3E-Net is based on a patch-wise network for feature extraction and image-wise networks for final image classification and uses an elastic ensemble based on Shannon Entropy as an uncertainty quantification method for measuring the level of randomness in image predictions. As the second contribution, the thesis presents a novel multi-level context and uncertainty-aware deep learning architecture named MCUa for the classification of breast cancer microscopic images. MCUa consists of multiple feature extractors and multi-level context-aware models in a dynamic ensemble fashion to learn the spatial dependencies among image patches and enhance the learning diversity. Also, the architecture uses Monte Carlo (MC) dropout for measuring the uncertainty of image predictions and deciding whether an input image is accurate based on the generated uncertainty score. The third contribution of the thesis introduces a novel model agnostic method (AUQantO) that establishes an actionable strategy for optimising uncertainty quantification for deep learning architectures. AUQantO method works on optimising a hyperparameter threshold, which is compared against uncertainty scores from Shannon entropy and MC-dropout. The optimal threshold is achieved based on single- and multi-objective functions which are optimised using multiple optimisation methods. A comprehensive set of experiments have been conducted using multiple medical imaging datasets and multiple novel evaluation metrics to prove the effectiveness of our three contributions to clinical practice. First, 3E-Net versions achieved an accuracy of 96.15% and 99.50% on invasive breast carcinoma dataset. The second contribution, MCUa, achieved an accuracy of 98.11% on Breast cancer histology images dataset. Lastly, AUQantO showed significant improvements in performance of the state-of-the-art deep learning models with an average accuracy improvement of 1.76% and 2.02% on Breast cancer histology images dataset and an average accuracy improvement of 5.67% and 4.24% on Skin cancer dataset using two uncertainty quantification techniques. AUQantO demonstrated the ability to generate the optimal number of excluded images in a particular dataset

Collaborative Brain-Computer Interfaces in Rapid Image Presentation and Motion Pictures

The last few years have seen an increase in brain-computer interface (BCI) research for the able-bodied population. One of these new branches involves collaborative BCIs (cBCIs), in which information from several users is combined to improve the performance of a BCI system. This thesis is focused on cBCIs with the aim of increasing understanding of how they can be used to improve performance of single-user BCIs based on event-related potentials (ERPs). The objectives are: (1) to study and compare different methods of creating groups using exclusively electroencephalography (EEG) signals, (2) to develop a theoretical model to establish where the highest gains may be expected from creating groups, and (3) to analyse the information that can be extracted by merging signals from multiple users. For this, two scenarios involving real-world stimuli (images presented at high rates and movies) were studied. The first scenario consisted of a visual search task in which images were presented at high frequencies. Three modes of combining EEG recordings from different users were tested to improve the detection of different ERPs, namely the P300 (associated with the presence of events of interest) and the N2pc (associated with shifts of attention). We showed that the detection and localisation of targets can improve significantly when information from multiple viewers is combined. In the second scenario, feature movies were introduced to study variations in ERPs in response to cuts through cBCI techniques. A distinct, previously unreported, ERP appears in relation to such cuts, the amplitude of which is not modulated by visual effects such as the low-level properties of the frames surrounding the discontinuity. However, significant variations that depended on the movie were found. We hypothesise that these techniques can be used to build on the attentional theory of cinematic continuity by providing an extra source of information: the brain