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Julien Fleureau

Bio: Julien Fleureau is an academic researcher from French Institute of Health and Medical Research. The author has contributed to research in topics: Haptic technology & Segmentation. The author has an hindex of 14, co-authored 37 publications receiving 600 citations. Previous affiliations of Julien Fleureau include University of Rennes & French Institute for Research in Computer Science and Automation.

Papers
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Journal ArticleDOI
TL;DR: A novel EMD approach, which allows for a straightforward decomposition of mono- and multivariate signals without any change in the core of the algorithm, is proposed, and Qualitative results illustrate the good behavior of the proposed algorithm whatever the signal dimension is.

75 citations

Journal ArticleDOI
TL;DR: This paper presents a new database for the analysis of valence (positive or negative emotions), which comprises physiological recordings and 257-channel EEG data, contrary to all previously published datasets, which include at most 62 EEG channels.
Abstract: Electroencephalography (EEG)-based emotion recognition is currently a hot issue in the affective computing community. Numerous studies have been published on this topic, following generally the same schema: 1) presentation of emotional stimuli to a number of subjects during the recording of their EEG, 2) application of machine learning techniques to classify the subjects’ emotions. The proposed approaches vary mainly in the type of features extracted from the EEG and in the employed classifiers, but it is difficult to compare the reported results due to the use of different datasets. In this paper, we present a new database for the analysis of valence (positive or negative emotions), which is made publicly available. The database comprises physiological recordings and 257-channel EEG data, contrary to all previously published datasets, which include at most 62 EEG channels. Furthermore, we reconstruct the brain activity on the cortical surface by applying source localization techniques. We then compare the performances of valence classification that can be achieved with various features extracted from all source regions (source space features) and from all EEG channels (sensor space features), showing that the source reconstruction improves the classification results. Finally, we discuss the influence of several parameters on the classification scores.

75 citations

Journal ArticleDOI
TL;DR: The techniques, formalisms, and key results pertinent to this medium of haptic-audiovisual (HAV) content are presented and the pressing necessity for evaluation techniques in this context is highlighted.
Abstract: Haptic technology has been widely employed in applications ranging from teleoperation and medical simulation to art and design, including entertainment, flight simulation, and virtual reality. Today there is a growing interest among researchers in integrating haptic feedback into audiovisual systems. A new medium emerges from this effort: haptic-audiovisual (HAV) content. This paper presents the techniques, formalisms, and key results pertinent to this medium. We first review the three main stages of the HAV workflow: the production, distribution, and rendering of haptic effects. We then highlight the pressing necessity for evaluation techniques in this context and discuss the key challenges in the field. By building on existing technologies and tackling the specific challenges of the enhancement of audiovisual experience with haptics, we believe the field presents exciting research perspectives whose financial and societal stakes are significant.

74 citations

Journal ArticleDOI
TL;DR: A real-time affect detector dedicated to video viewing and entertainment applications that combines the acquisition of traditional physiological signals, namely, galvanic skin response, heart rate, and electromyogram, and the use of supervised classification techniques by means of Gaussian processes is proposed.
Abstract: In this paper, we propose a methodology to build a real-time affect detector dedicated to video viewing and entertainment applications. This detector combines the acquisition of traditional physiological signals, namely, galvanic skin response, heart rate, and electromyogram, and the use of supervised classification techniques by means of Gaussian processes. It aims at detecting the emotional impact of a video clip in a new way by first identifying emotional events in the affective stream (fast increase of the subject excitation) and then by giving the associated binary valence (positive or negative) of each detected event. The study was conducted to be as close as possible to realistic conditions by especially minimizing the use of active calibrations and considering on-the-fly detection. Furthermore, the influence of each physiological modality is evaluated through three different key-scenarios (mono-user, multi-user and extended multi-user) that may be relevant for consumer applications. A complete description of the experimental protocol and processing steps is given. The performances of the detector are evaluated on manually labeled sequences, and its robustness is discussed considering the different single and multi-user contexts.

70 citations

Proceedings ArticleDOI
04 Mar 2012
TL;DR: A complete framework to both produce and render the motion embedded in an audiovisual content is proposed to enhance a natural movie viewing session and a complete methodology to evaluate the relevance of this framework is proposed.
Abstract: This work aims at enhancing a classical video viewing experience by introducing realistic haptic feelings in a consumer environment. More precisely, a complete framework to both produce and render the motion embedded in an audiovisual content is proposed to enhance a natural movie viewing session. We especially consider the case of a first-person point of view audiovisual content and we propose a general workflow to address this problem. This latter includes a novel approach to both capture the motion and video of the scene of interest, together with a haptic rendering system for generating a sensation of motion. A complete methodology to evaluate the relevance of our framework is finally proposed and demonstrates the interest of our approach.

48 citations


Cited by
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Journal ArticleDOI
TL;DR: This paper attempts to survey and summarize the recent research and development of EMD in fault diagnosis of rotating machinery, providing comprehensive references for researchers concerning with this topic and helping them identify further research topics.

1,410 citations

Journal ArticleDOI
TL;DR: An overview of HSMMs is presented, including modelling, inference, estimation, implementation and applications, which has been applied in thirty scientific and engineering areas, including speech recognition/synthesis, human activity recognition/prediction, handwriting recognition, functional MRI brain mapping, and network anomaly detection.

734 citations

Journal ArticleDOI
TL;DR: This paper presents an extensive review on the artifact removal algorithms used to remove the main sources of interference encountered in the electroencephalogram (EEG), specifically ocular, muscular and cardiac artifacts, and concludes that the safest approach is to correct the measured EEG using independent component analysis-to be precise, an algorithm based on second-order statistics such as second- order blind identification (SOBI).
Abstract: This paper presents an extensive review on the artifact removal algorithms used to remove the main sources of interference encountered in the electroencephalogram (EEG), specifically ocular, muscular and cardiac artifacts. We first introduce background knowledge on the characteristics of EEG activity, of the artifacts and of the EEG measurement model. Then, we present algorithms commonly employed in the literature and describe their key features. Lastly, principally on the basis of the results provided by various researchers, but also supported by our own experience, we compare the state-of-the-art methods in terms of reported performance, and provide guidelines on how to choose a suitable artifact removal algorithm for a given scenario. With this review we have concluded that, without prior knowledge of the recorded EEG signal or the contaminants, the safest approach is to correct the measured EEG using independent component analysis-to be precise, an algorithm based on second-order statistics such as second-order blind identification (SOBI). Other effective alternatives include extended information maximization (InfoMax) and an adaptive mixture of independent component analyzers (AMICA), based on higher order statistics. All of these algorithms have proved particularly effective with simulations and, more importantly, with data collected in controlled recording conditions. Moreover, whenever prior knowledge is available, then a constrained form of the chosen method should be used in order to incorporate such additional information. Finally, since which algorithm is the best performing is highly dependent on the type of the EEG signal, the artifacts and the signal to contaminant ratio, we believe that the optimal method for removing artifacts from the EEG consists in combining more than one algorithm to correct the signal using multiple processing stages, even though this is an option largely unexplored by researchers in the area.

640 citations

Journal ArticleDOI
TL;DR: In this article, the authors reviewed the recent advances in Raman spectroscopy and its new trend of applications ranging from ancient archaeology to advanced nanotechnology, including the aspects of Raman measurements to the analysis of various substances categorized into distinct application areas such as biotechnology, mineralogy, environmental monitoring, food and beverages, forensic science, medical and clinical chemistry, diagnostics, pharmaceutical, material science, surface analysis etc.

461 citations

Journal Article
TL;DR: Alain Berthoz takes the reader on a whirlwind tour of cognitive neuroscience topics: perception, coherence, memory, prediction, and adaptation, and builds a persuasive case supporting his thesis that the brain is an anticipation machine.
Abstract: The Brain's Sense of Movement. By Alain Berthoz (Translated by Giselle Weiss). Cambridge, Massachusetts: Harvard University Press; 2000, 352 pp. $22.80. Ever wonder how certain people catch or bat a baseball hurled at blurring speeds? If you have, find yourself in a group whose intended or accidental success maybe a machine that pitches and throws like a ballplayer. Once this group of researchers articulates an accurate set of principles behind movement, deft engineering, persistence, and luck may converge to emulate nature. Although Berthoz's The Brain's Sense ofMovement, does not offer a science-fiction glimpse of agile androids that populate Asimov's novels, it provides an organized and fascinating way of thinking about movement. Berthoz takes the reader on a whirlwind tour of cognitive neuroscience topics: perception, coherence, memory, prediction, and adaptation. By examining these topics and using choice examples, Berthoz builds a persuasive case supporting his thesis that the brain is an anticipation machine. Even before delving into the intricacies of each of these topics, Berthoz's claim seems reasonable in light of evolution. In fact, Berthoz explains how evolution and improved neural systems that guide movement influence and drive each other: \"The species that passed the test of natural selection are those that figured out how to save a few milliseconds in capturing prey and anticipating the actions of predators, those whose brains were able to simulate the elements of the environment and choose the best way home, those able to memorize great quantities of information from past experience and use them in the heat of action.\" This cat and mouse games has honed the brain to take advantage of its parallel architecture, bypassing computing each trajectory in a Newtonian sense, and arriving at a solution by using heuristics developed over evolution. Heuristics play an important role in examples where a target exceeds physical limits of detection. For example, a baseball may move too quickly for the fovea to focus, however, the brain, and skeletal-muscular system use computational shortcuts to simulate, predict, adapt, and control the body in response to a changing environment. The first choice example that Berthoz highlights as a key computational shortcut is the derivative. Signals from receptors enable anticipation of future position of the head owing to their sensitivity to derivatives such as jerk, acceleration, and velocity. Another mathematical concept that Berthoz explores as a predictive tool is tensors. From what I learned, a tensor is a group of mathematical operators called matrices that carry out transformations among vectors. Between derivatives and vectors, Berthoz devotes several chapters to explaining how otoliths and semicircular canals use derivatives for linear and angular accelerations to predict while tensors receive perfunctory treatment. A balance between these two topics may better satisfy some readers. Certainly, derivatives and tensors alone cannot account for movement. Just as a calculator or computer derives its usefulness in a network, mathematical shortcuts for movement need to occur in the context of a circuit. Reading Berthoz's

291 citations