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Keynote Lectures

Voice as a Vehicular Tool to Organic and Neurological Disease Tracking: How Far we May Go?
Pedro Gómez Vilda, Universidad Politécnica de Madrid, Spain

Source Separation for Biomedical Signals: Blind or not Blind?
Christian Jutten, GIPSA-lab, France

The Present and Future of Devices for Neural Recording
Adam Kampff, Champalimaud Foundation, Portugal

All a Matter of Timing: Neuroscience and Neural Engineering of Abnormal Human Movement
Richard Reilly, Trinity College Dublin, Ireland

Multimodal Interfaces: Capture, Tracking and Recognition
Vladimir Devyatkov, Bauman Moscow State Technical University, Russian Federation

Methodologies for Systems Medicine: Time to Join the Forces of Bioengineering and Bioinformatics
Pietro Lio, University of Cambridge, United Kingdom

 

Voice as a Vehicular Tool to Organic and Neurological Disease Tracking: How Far we May Go?

Pedro Gómez Vilda
Universidad Politécnica de Madrid
Spain
 

Brief Bio
Pedro received the M.Sc. degree in Communications Engineering in 1978 and the Ph.D. degree in Computer Science from the Universidad Politécnica de Madrid, Madrid, Spain, in 1983. He is Professor in the Computer Science and Engineering Department, at Universidad Politécnica de Madrid since 1988. His current research interests are biomedical signal processing, speaker identification, cognitive speech processing, and neurological disease detection and monitoring from speech and voice. He has over 250 referenced publications and papers, and has directed 15 PhD Theses. He has participated in around 50 research projects (30 as main researcher). He is the head of the Group of Informatics in Signal and Image Processing at Universidad Politécnica de Madrid integrated in the Biomedical Technology Center. Dr. Gómez Vilda is a member of the IEEE, ISCA and EURASIP.


Abstract
Voice and voiced speech are biological signals produced by humans with communication purposes. Current signal processing methods allow the inversion of the voice producing apparatus to deepen into biomechanical systems driven by neuromotor signals. Complex inverse models may be designed and set-up to reconstruct semantically rich correlates which are related with organic as well as neurologic disease etiology. New detecting, tracking and monitoring tools and protocols may help in improving current protocols to help in the care of illnesses as Parkinson, Alzheimer or Lateral Amiotrophic Schlerosis, or in Voice Rehabilitation of the Laryngectomized, or in evaluating function restoration after Larynx Conservative Surgery, as well as in Singing Education and many other fields. The talk concentrates in discussing the basic methodologies and algorithms to focus on protocols, tools and results in the state-of-the-art.



 

 

Source Separation for Biomedical Signals: Blind or not Blind?

Christian Jutten
GIPSA-lab
France
 

Brief Bio

Christian Jutten received the PhD degree in 1981 and the Docteur ès Sciences degree in 1987 from the Institut National Polytechnique of Grenoble (France). He taught as associate professor in the Electrical Engineering Department from 1982 to 1989, before to become full professor in University Joseph Fourier of Grenoble, more precisely in the sciences and technologies department: Polytech’Grenoble. He was visiting professor in Swiss Federal Polytechnic Institute in Lausanne in 1989 and in Campinas University (Brazil) in 2010. He has been associate director of the Grenoble images, speech, signal and control laboratory (GIPSA, 300 people) and head of the Department Images-Signal (DIS) of this laboratory, from 2007 to 2010. For 30 years, his research interests are blind source separation, independent component analysis and learning in neural networks, including theoretical aspects (separability, source separation in nonlinear mixtures, sparsity) and applications in signal processing (biomedical, seismic, hyperspectral imaging, speech).  He is author or co-author of more than 65 papers in international journals, 4 books, 19 invited plenary talks and 150 communications in international conferences. He has been associate editor of IEEE Trans. on Circuits and Systems (1994-95), and co-organizer the 1st International Conference on Blind Signal Separation and Independent Component Analysis (Aussois, France, January 1999). He has been a scientific advisor for signal and images processing at the French Ministry of Research from 1996 to 1998 and for the French National Research Center (CNRS) from 2003 to 2006. He is a member of the technical committee “Blind signal Processing” of the IEEE CAS society and of the technical committee “Machine Learning for signal Processing” of the IEEE SP society. He is a reviewer of main international journals (IEEE Trans. on Signal Processing, IEEE Signal Processing Letters, IEEE Trans. on Neural Networks, Signal Processing, Neural Computation, Neurocomputing, etc.) and conferences in signal processing and neural networks (ICASSP, ISCASS, EUSIPCO, IJCNN, ICA, ESANN, IWANN, etc.). He received the EURASIP best paper award in 1992 and Medal Blondel in 1997 from SEE (French Electrical Engineering society) for his contributions in source separation and independent component analysis, and has been elevated as a Fellow IEEE and a senior Member of Institut Universitaire de France in 2008.


Abstract
Source separation is a fundamental problem in signal processing. In early 80's, independent component analysis (ICA) have been developed for solving this problem, with very weak priors on sources hence the name "blind". ICA was then successfully applied in middle of 90 to biomedical signals, like ECG, EEG, MRI, etc. But, in fact, each class of  biomedical signals has usually known properties, which can be used as priors in source separation, and in addition (or instead) to source independence which is not always satisfied. Usual priors can be properties in frequency or time domains, model of sources. Using such priors, the source separation problem becomes semi-blind (or not blind at all !) and one can consider (or design) methods which are simpler and more efficient than ICA. In this talk, I first explain the main theoretical results for solving source separation using ICA or methods based on various priors. Then, I illustrate the various approaches proposed in the theoretical framework with a few examples in biomedical signal processing. The talk will finish with a discussion on a few open questions.



 

 

The Present and Future of Devices for Neural Recording

Adam Kampff
Champalimaud Foundation
Portugal
 

Brief Bio

Adam Kampff studied astrophysics at Harvard University in Cambridge, MA. He spent many hours at the observatory where he worked the overnight shift at a radio telescope and thought about the brain. Adam stayed at Harvard for his PhD in neuroscience, during which he designed and built two-photon laser scanning microscopes and investigated neural circuits that control visual behavior. After graduating in 2009 and receiving a postdoctoral fellowship from Harvard's Mind, Brain and Behavior program, Adam began studying how the mammalian brain controls and learns motor behaviors. In 2011, Adam moved to Lisbon, Portugal to start his own laboratory at the Champalimaud Centre for the Unknown. The goal of his research is to understand how a nervous system constructs a model of the world: How do brains learn about the statistics of their environment? How is this information encoded in networks and used to control intelligent behavior? Answers to these fundamental questions will require the development of novel devices for simultaneously recording from large populations of neurons throughout the brain of a behaving animal. Therefore, in parallel with his neuroscience investigation of mammalian cortex, Adam's lab also utilizes techniques from microfabrication and microelectronics to design and construct new sensors for recording neural activity.


Abstract
The brain is a network of individual neurons, each transmitting electrical impulses and communicating via chemical signals. Monitoring the activity of these neurons is necessary to understand how the networks of the brain process sensory information and control behavior. There are many, many neurons in these networks (a cubic millimeter of brain tissue may contain ~100,000 neurons), yet current devices for recording their electrical activity monitor only a small fraction (~10 neurons per cubic millimeter). To progress our understanding of the function and pathology of the brain, we will require new devices capable of measuring neuron electrical activity at a network scale, as well as devices to detect the chemical signals that communicate and regulate this activity. I will discuss new approaches to build such devices, which borrow from advances in nano- and microfabrication, MEMS, and biosensors. While the application of these technologies to neuroscience holds great promise, many obstacles remain. New insights, from many different technical fields, will be required to produce the devices that allow us to transcend our current understanding of nature's most complex machine.



 

 

All a Matter of Timing: Neuroscience and Neural Engineering of Abnormal Human Movement

Richard Reilly
Trinity College Dublin
Ireland
 

Brief Bio

Richard Reilly is Professor of Neural Engineering at Trinity College Dublin, a joint position between the School of Medicine and School of Engineering. He is also Director and a Principal Investigator of the Trinity Centre for Bioengineering and also a Principal Investigator at the Trinity College Institute of Neuroscience.  His research focuses on the processing of signals that diagnose the human physiological and cognitive state. His research has uncovered non-invasive electrophysical biomarkers for cognitive function, and has created patient-oriented neurodiagnostics methods, neural prosthetics and therapeutic neuromodulation devices. His research inputs to the cross-disciplinary translational research at Trinity College on ageing and neurodegeneration specifically though the Department of Medical Gerontology and the Mercer’s Institute on Successful Ageing at St. James's Hospital, Dublin. He is a member of the Royal Irish Academy and a Senior Member of the Institute of Electrical and Electronics Engineers. He is currently the President of the European Society of Engineering and Medicine, a member of the Board of Tallaght Hospital in Dublin and a member of the Irish Medicine Board’s Advisory Committee on Medical Device. In 2004, he was awarded a US Fulbright Award for research collaboration into multisensory integration with the Nathan Kline Institute for Psychiatric Research, New York. He is a former Silvanus P. Thompson International Lecturer for the Institution of Engineering and Technology. He has two start-up companies based on his research activities.Professor Reilly received his BE degree in Electronic Engineering, a MEngSc and a PhD in Biomedical Engineering from University College Dublin.  Professor Reilly has authored or co-authored over 275 peer-reviewed publications. 



Abstract
Human movement involves a complex series of coordinated neural and muscloskeletal processes. A breakdown in any of these processes can result in abnormal movement. Movement Disorders collectively affect approximately 10 million people in Europe. Parkinson’s disease, essential tremor and primary dystonia are the three most common movement disorders in Europe. 500,000 people have some form of dystonia, of which 80,000 have primary dystonia. This talk will discuss results of recent behavioral and neuroimaging experiments to help better understand the pathogenesis and genetic basis and of dystonia. The talk will also discuss the analysis of movement recorded in realworld environments to provide a window into cognitive function.



 

 

Multimodal Interfaces: Capture, Tracking and Recognition

Vladimir Devyatkov
Bauman Moscow State Technical University
Russian Federation
 

Brief Bio
Twenty five  years  with  Institute  of  Control  Sciences  of  Russia  Academy of Sciences during  which  moved  from  assistant  to  the  head  of   laboratory   of   Intellectual    logical  control  systems. Twenty  years  with Bauman Moscow  Technical University as Heard of  the Information Systems and Telecommunication Department. D.Sc. degree  in  control  engineering  from the Institute of Control Sciences of USSR Academy of Sciences, Moscow, USSR, 1980. Ph.D degree in technical cybernetics from the Institute of Control Sciences of USSR Academy of Sciences, Moscow, USSR, 1970. Diploma in electrical engineering from the Institute of  Precise  instruments  and  Optical devices, Leningrad, USSR, 1963. Active member of the International Information Academy. Deputy editor of the periodical "BMSTU VESTNIC".  Vise-president of Moscow Instrument Society. The member of Science Councils of Bauman Moscow  Technical University and Institute  of  Contol  Sciences  of  Russia  Academy of Sciences, The member of International Program Committee of ISPE/IEE/IFAC International Conference on CAD/CAM, Robotics and Factories of the Future CARS & FOF’S. Member of the International Institute of Informatics and Systemic, USA. He has more 120 scientific papers. His main research interests are artificial intelligence, fuzzy logic and its application, the theory of finite-state machines, computer vision and artificial intelligence.


Abstract
A main goal of multimodal interface now is to support natural, efficient, powerful, and flexible human-computer interaction for different types of virtual environments.  If the interaction technology is awkward, or constraining, the user’s experience with the synthetic environment is severely degraded. If the interaction itself draws attention to the technology, rather than the task at hand, it becomes an obstacle to a successful virtual environment interface. The traditional two-dimensional, keyboard- and mouse-oriented graphical user interface (GUI) is not well-suited for virtual environments. Instead, using several different modalities and integrating them provide the opportunity to develop user-friendly interface with a virtual environment. The cross product of communication modalities and sensing devices begets a wide range of multimodal interface techniques. The potential of these techniques to support natural and powerful interfaces is the future of virtual reality constructing and designing. To more fully support natural communication, it has to not only track human movement, but to interpret that movement in order to recognize semantically meaningful modality. While tracking user’s modalities may be quite useful to express them through higher-level relations such as distance, the relative direction of movement or orientation of the objects being tracked.

In this lecture, we shall consider the most popular approaches for capture, tracking and recognition of different modalities simultaneously to create intellectual human-computer interface for different goals. Taking into account the large gesture variability and their important role in creating intuitive interfaces, the considered approaches focus one's attention on gestures although the approaches may be used also for other modalities. The considered approaches are user independent and do not require large learning samples. In section 1 of the lecture gestures modalities are considered as natural and artificial. Before gestures recognition the parts of the body involved in gestures have to be captured in video stream.  Modern capture and tracking methods are included in section 2 of the lecture. If the part of the body has been captured as a digital image, it can be recognized using some mathematical recognition models. Section 3 of the lecture is devoted to the most effective recognition models. Multimodal aggregation as a way to an intellectual human-computer interaction is presented in section 4 of the lecture. The last section of the lecture is a conclusion.



 

 

Methodologies for Systems Medicine: Time to Join the Forces of Bioengineering and Bioinformatics

Pietro Lio
University of Cambridge
United Kingdom
 

Brief Bio
Not available


Abstract
Systems medicine is an exciting new field which will provide effective answers to the challenges of integrating Big Data (meta analysis of datasets, multi omics, multi organs), characterize comorbidities, and building a multiscale model of human physiology.

Today multi-scale and complex biomedical data are gathered and analysed in a rather simple way that in many cases misses the opportunity to uncover combinations of predictive disease profiles; always the subtle associations about comorbidities found in the meta data analysis need bioinformatics to suggest the most appropriate experimental validation. Modeling is superior to the data-mining correlative approach in transforming data into knowledge but data still should be used for parameter estimation. This is particularly true both in computational systems biology and bioengineering where mechanistic models, based on deterministic and stochastic differential equations, could be devised from biological experimental knowledge.

Here the problem we encounter is of a different kind: we can observe what happens at almost all scales, from the whole organism down to the molecular level; however, putting things together in order to obtain real understanding is much more difficult and less developed.

One important aspect of multi scale modeling is the homogenisation of models across multi spatial scales, which allow cell-level models (using ODE or agent based) to be systematically scaled up to the tissue/organ level, and related asymptotic techniques for the analysis of multiple timescale problems.

Here the challenge is the model order reduction, i.e. to abandon high dimensional bioengineering systems in favour of the simplest mathematical model that “does the job”. Again the help comes from the bioinformatics analysis of the data which, leveraging on millions year evolution, describes the space of solutions explored by Nature. We discuss the powerful connection between bioinformatics and bioengineering through two case studies: the immune system response and the biomechanics of bone remodeling.  Both systems provide interesting examples of Big Data, comorbidities and multi scale systems.



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