Guide to Brain- Interfacing Eduardo Reck Miranda Julien Castet Editors

Guide to Brain-Computer Music Interfacing

123 Editors Eduardo Reck Miranda Julien Castet Interdisciplinary Centre for Computer Immersion Music Research (ICCMR) Bordeaux Plymouth University France Plymouth UK

ISBN 978-1-4471-6583-5 ISBN 978-1-4471-6584-2 (eBook) DOI 10.1007/978-1-4471-6584-2

Library of Congress Control Number: 2014946764

Springer London Heidelberg New York Dordrecht

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Springer is part of Springer Science+Business Media (www.springer.com) Foreword

Brain-Computer Music Interfacing (BCMI): One Place Where Science and Music May Meet in Deep Theoretical Territory

There is little doubt that as we gain facility in the intense, disciplined practices required to probe the origins of musical impulses, pathways of emergence can be observed and experienced by tracing backward down trails left in the normally outward flowering of forms from the brain, from their initial neural geneses, upward through their manifestations in neural network holarchies,1 and finally to their manifestations in localized, time‐space expressions. Along these pathways, the languaged forms of the presumably testable theoretical models of science and the investigative, speculative models of propositional music converge. Propositional music involves a point of view about composing in which com- posers might build proposed models of worlds, universes, evolution, brains, con- sciousness or whole domains of thought and life, and then proceed to make dynamical musical embodiments of these models, inviting us to experience them in spontaneously emerging sonic forms (Rosenboom 2000a). For musicians who are interested in deep conceptual and theoretical investigations, BCMI is a natural attractor and a predictable outgrowth from mid-twentieth century explosions in interdisciplinary thinking, Cybernetics, Artificial Intelligence, Linguistics, Systems Theory, self-organization, morphogenesis, algorithmic music, and so on. Following early explorations in BCMI from that time, we are now experiencing a new flowering of what might be called antidisciplinary thinking in the arts and sciences, which, among other things, reexamines fundamental distinctions among scientific and cultural languages (Beech 2013). Some extant model paradigms that employ BCMI in artistic creation can legit- imately claim to be new musical propositions. Others focus more on direct map- pings of neurological data onto acoustic parameters of sound or components of traditionally familiar musical structures. Both may reveal fruitful investigative

1 The term holarchy is used to refer to structures that have both top-down and bottom-up dynamical aspects.

v vi Foreword pathways; and there is a big difference between them. Direct mapping, often called sonification, draws on the profoundly integrative powers of auditory perception— possibly enhanced by musical experience and training—to hear relationships and find clues to hidden orders and unsuspected patterns. With careful, active imagi- native listening, we can learn to distinguish the features of complexity and parse subtle relationships among differentiable, complex entities. Our auditory perception and cognition toolkits can be fine-tuned in these respects to extraordinary degrees. Later, we can probe these newly perceived patterns and quantify them with other investigative tools. Aesthetic propositions, on the other hand, may probe the very nature of thought, itself—in this case musical thought—and its symbiotic inhabiting of the brain in co-creative, adaptive, emergent, feedback-driven phenomena. Cir- cling back to scientific exploration, these musical BCMI propositions may fuel important investigations into the nature of prediction, presumed causal relation- ships, ways to understand the global functions of the brain and other insights leading to paradigm shifts, from which even new practical applications may follow. Here again, the integrative powers of musical perception and musical cognition may be brought to bear on deep aesthetic investigations, which may, in return, offer important insights and clues for scientific explorations and theoretical modeling. Musical BCMI propositions, if we are alert, may take us back to first principles again and again, questioning our understanding of evolution and categorization. Recalling Charles Sanders Peirce’s doctrine of dicisigns, we might soon discover a biomusical semiotics emerging from neuro-musical propositions. See (Stejernfelt 2014) for an analysis of the doctrine of dicisigns. We should be careful, though, to avoid the potential misconception of BCMI as MCMI (Mind-Computer Music Interfacing). At the moment, we don’t really know any more about what the mind is than we know about what energy is. Richard Feynman reminds us that all we really know about energy is that it is related to some quantity we can calculate that doesn’t change when things happen (conser- vation law) (Feynman 1995). Similarly, we don’t really have a good characteriza- tion of what mind is, except we claim to be able to sense its presence. We do have many interesting speculative theories about the mind’s emergence (Swan 2013), and some may pan out. It may be that minds have evolved to be able to know—perhaps an illusion—what other minds are thinking and feeling.2 Perhaps the concept of mind equates to what we believe we can know as being relatively constant when we are observing relationships among entities that we differentiate as individually unique and label as being conscious. Is mind a constraint-based, emergent phe- nomena that might have begun already with proto-life forms (Deacon 2013)? And what about intelligence? Nobody seems to really know clearly what it is, but everyone believes they can tell when it is absent. Are intelligences differentiable and knowable in advance of their encounter? Perhaps intelligence might be also

2 I’ve been thinking about this a lot after discussing it with cognitive scientist (also a musician), Scott Makeig, Driector of the Swartz Center for Computational at the University of California, San Diego (UCSD). Foreword vii considered as a field,orintellisphere, perhaps even on inter-stellar scales (Rosen- boom 2003b). Stjernfelt invokes the term cognitive field in describing Peirce’s undoing of the misleading dualisms that dangerously pocket this intellectual terrain, particularly when considering causal modeling (Stjernfelt 2014). Yet, how do we describe what’s going on in the brain in holistic terms, the interacting of neural atoms in a macro-form? Here again we have propositional language issues. There is imprecision in propositions, though imprecise meanings also have value. The term, field, has a precise meaning, as in vector field, but also imprecise ones, as in the nature of chreods or zones of influence (Thom 1975). Quantum paradigms have taught us that the universe is not a precision instrument. Imprecision and approx- imation cannot be overcome. Rather, though, they have value in permitting guided explorations into the fringes of thought. So BCMI is striving to balance its need for precision in developing practical applications with its also critical need to explore imprecise paradigms, which often enable breakthroughs in thought and vision. BCMI is destined to open new doors, as long as we remain open to the unpredictable. We need to gain better under- standing of global complexity in both brains and music. BCMI may offer useful tools for this. Music is fundamentally about time, and therefore about qualities of change. The spatiotemporal evolution of holistic brain phenomena is also about qualities of change. This is a good match for unveiling the future. My own work with BCMI began in the 1960s (Rosenboom 1972, 1976a, 1976b, 1977, 1990, 1997, 2000b, 2000c, 2003a, 2006). Recently, this work has resurfaced, re-energized by new, accessible technology, and significant advances in methods for analyzing brain signals. The most recent example is a new composition called Ringing Minds, created in 2014 in collaboration with Tim Mullen and Alex Khalil at the Schwartz Center for Computational Neuroscience in the University of California at San Diego (UCSD). Ringing Minds builds on techniques for extracting principal oscillation patterns (POPs or eigenmodes) from maximally independent sources of intracranial electroencephalogram (EEG) data (Mullen et al. 2012). These tools were originally developed for epilepsy research. Mullen adapted them to analyze EEG data from an ensemble of five brain music performers. We treated the data as if it were arising from a collective brain, a five-person brain, and extracted eigenmode data in which we could identify distributions of these resonant modes across the collective brain. We also extracted event-related potentials (ERPs) by averaging simultaneous signals spatially, across the five brains, instead of across time with a single brain. In this way, we hoped to extract pointers to attention shifts in the five-performer group resulting from musical events emanating from two improvising musicians on stage. I played electric violin, and Khalil built and played the lithophone, an instrument that looks like a xylophone, but made with stone slabs struck with a hammer. I built a software-based instrument for this work, the central core of which is a very large array of complex resonators that can respond to the collective EEG eigenmode data in a way that generates a vast sound field of ringing components. The control parameters of the instrument can also be varied in real-time performance. The instrument is, in effect, a compositional model viii Foreword inspired by the analytical model working on the EEG signals from the five-brain performing group. The model thus becomes an instrument. Ringing Minds investigates many things. Among them are complex relationships manifesting among the components of a sound environment—the resonator field— and a group of individuals, who may interact with this environment via natural listening patterns and/or use biofeedback techniques to try to influence that envi- ronment. With careful, active imaginative listening to the results of this fine-grained resonant field, one can imagine witnessing both local and global processes inter- acting and perceive small-scale, quantum processes zooming out into larger scale arenas of human perceptibility. The local is mirrored in the global and is impressed on the environment, which bears witness to both the emergence of coherence among components and the loss of coherence. This propositional neuromusical model is analogous to an intriguing propositional physical model that relates objective and subjective quantum states to their environment as witness (Ollivier et al. 2004). This new volume, Guide to Brain-Computer Music Interfacing, offers a won- derful collection of BCMI tools with which adventuresome explorers may pursue both practical and propositional models in both neuromusic and music neurosci- ence. In an introductory chapter, Eduardo Reck Miranda contextualizes what fol- lows under an umbrella with three major challenges: extracting meaningful control information from brain signals, designing effective generative music techniques that respond to this information, and effectively improving peoples lives. That’s a tall order, but achievable with today’s tools. Important techniques for prosthetics, device control, and handsfree interaction with computers follow. Very important work on event-related potentials (ERPs), especially with P300 waves, is included. P300 analysis was critical to my 1970s work on detecting possible neural correlates of attention shifts associated with musical features and forms (Rosenboom 1977, 1990, 1997, 2000b, 2000c). In that early work a combination of template matching and signal averaging techniques—then implemented with real-time, hardware computing devices—was used to shorten the normally long latencies separating targeted features in musical (or raw acoustic) forms from the points in time when their ERP concomitants could be reliably observed. A means for calculating pre- dicted expectancy quantifications for events in sequences was also employed and used to predict when and where ERPs with strong P300 components were likely to occur. These predictions identified time points where analysis algorithms would be triggered. Feedback about successful and unsuccessful predictions was then used to influence sound synthesis and compositional form-generating algorithms. Later in this volume, questions are explored about semiotic BCI—recalling Peirce again—and the use of machine learning to dig into relationships among music and emotions. This is complex and delicate territory rife with presumptions about musical emotions that need clarification; and some of the tools offered here may be helpful in that quest. Excellent tutorials on signal extraction, brain electric fields, passive BCI, and applications for genetic algorithms are offered along with historical surveys. In a penultimate chapter, Miranda and colleagues return to describe how BCMI research has received motivation from health and medical Foreword ix sectors as well as the entertainment industry and to advocate for the importance of “the potential impact on musical creativity of better scientific understanding of the brain, and the development of increasingly sophisticated technology to scan its activity.” This book opens many doors.

Valencia, US David Rosenboom

References

Beech A (2013) Science and its fictions. Notes from a talk on scientific Method, California Institute of the Arts, private communication, Valencia, CA Deacon TW (2013) Incomplete nature, how mind emerged from matter. W. W. Norton & Co, New York Feynman RP (1995) Six easy pieces. Addison-Wesley Pub. Co, Reading, MA Mullen T, Worrell G, Makeig S (2012) Multivariate principal oscillation pattern analysis of ICA sources during seizure. Proceedings of the 34th Annual International Conference of the IEEE, EMBS San Diego, CA Ollivier H, Poulin D, Zurek WH (2004) Objective properties from subjective quantum states: environment as witness. Phys Rev Lett PRL 93:22040-1–220401-4 Rosenboom D (1972) Methods of producing sounds or light flashes with alpha brain waves for artistic purposes. Leonardo 5, 1. In: Malina FJ (ed) (1973) Kinetic art. Dover Pub, New York, pp 152–156. Space Design 10 (Japanese translation) Tokyo: Kajima Institute Pub. Co, Tokyo, 1974 Rosenboom D (1976a) Brainwave music. LP record. Aesthetic Research Centre Records #ST1002, Toronto Rosenboom D (1976b) Biofeedback and the arts, results of early experiments. Aesthetic Research Centre of Canada Publications, Vancouver Rosenboom D (1977) On being invisible. LP record. Music Gallery Editions #MGE-4, Toronto Rosenboom D (1990) The performing brain. Computer Music Journal 14(1):48–66 Rosenboom D (1997) Extended musical interface with the human nervous system: assessment and prospectus. In: Revised electronic monograph. Original, Leonardo Monograph Series, 1, San Francisco, 1990 http://www.davidrosenboom.com/media/extended-musical-interface-humannervous- system-assessment-and-prospectus Rosenboom D (2000a) Propositional music: on emergent properties in morphogenesis and the evolution of music, essays, propositions, commentaries, imponderable forms and composi- tional method. In: Zorn J (ed) Arcana, musicians on music, Granary Books/Hips Road, New York, pp 203–232 Rosenboom D (2000b) Invisible gold, classics of live electronic music involving extended musical interface with the human nervous system. Audio CD. Pogus Productions, Chester, New York, P 21022-2 Rosenboom D (2000c) On being invisible II (Hypatia speaks to Jefferson in a dream). On: Transmigration music. Audio CD. Consortium to Distribute Computer Music, vol 30, #CRC 2940. Centaur Records, Inc., Baton Rouge, LA Rosenboom D (2003a) Propositional music from extended musical interface with the human nervous system. In: Avanzini G et al (eds) The and music. Annals of the New York Academy of Sciences, vol 999. New York Academy of Sciences, New York, pp 263–271 x Foreword

Rosenboom D (2003b) Collapsing distinctions: interacting within fields of intelligence on interstellar scales and parallel musical models. David Rosenboom Publishing, Valencia, CA http://www.davidrosenboom.com/media/collapsing-distinctions-interactingwithin-fields- intelligence-interstellar-scales-and Rosenboom D (2006) Brainwave music 2006. Audio CD. EM Records #EN1054CD, Osaka, Japan Stjernfelt F (2014) Natural propositions, the actuality of Peirce’s doctrine of dicisigns. Docent Press, Boston, MA Swan L (2013) Origins of mind. Biosemiotics 8. Springer, Dordrecht Thom R (1975) Structural stability and morphogenesis. W.A. Benjamin Inc., Reading, MA Preface

The idea of using brainwaves to make music dates back from the 1960s, when such as Alvin Lucier, Richard Teitelbaum, and David Rosemboom, to cite but three, looked into generating music with the electroencephalogram, abbreviated as EEG. Lucier placed electrodes on his own scalp, amplified the signals, and relayed them through loudspeakers that were “directly coupled to percussion instruments, including large gongs, cymbals, tympani, metal ashcans, cardboard boxes, bass and snare drums” (Lucier 1980). The low frequency vibrations emitted by the loud- speakers set the surfaces and membranes of the percussion instruments into vibration. Teitelbaum used various biological signals including the EEG and ECG (electro- cardiogram) to control electronic synthesisers (Teitelbaum 1976). Rosemboom sub- sequently looked into designing more sophisticated systems inspired by Cybernetics, exploring the concept of biofeedback in real-time music making (Rosenboom 1990). Those pioneering composers left an important legacy of concepts and practices. However, apart from very few sparse initiatives here and there, the idea seems to have faded into oblivion until the end of the twentieth century. We reckon that one of the reasons for this stagnation is that EEG equipment was not as widely available as it is today. Moreover, techniques for analyzing EEG signals were not as well developed as they are today, and consequently we lacked sophisticated handling and understanding of the EEG. A notable development for musicians was the appearance of a piece of equip- ment called BioMuse in the 1990s, manufactured by Benjamin Knapp and Hugh Lusted (1996). BioMuse provided a portable kit for digitally processing bio-signals such as the EEG, muscle movement, heartbeat, and so on. It was able to convert these signals into MIDI data, which facilitated the implementation of MIDI con- trollers using the EEG. Within the last two decades or so, we have witnessed the emergence of the field of Brain-Computer Interfacing, or BCI (also referred to as Brain-Machine Inter- facing, or BMI). Research into BCI is aimed at the development of technology to enable people control machines by means of commands expressed by signals, such as the EEG, detected directly from their brain. Most of this research is developed within Biomedical Engineering and is aimed at giving severely paralyzed patients the ability to control artificial limbs, wheel chairs, robotic equipment, machines, and

xi xii Preface so on. Obviously, in these cases, the user must be able to actually control these devices voluntarily and as precisely as possible. The user needs to produce specific patterns of EEG to command a machine and such a machine needs to interpret those patterns and do what the user wants it to do. Continuing progress in BCI research combined with the emergence of more affordable EEG equipment are fostering a renaissance of approaches to making music with brain signals: the field of Brain-Computer Music Interfacing, abbrevi- ated as BCMI, is now well established (Miranda 2010). The field of BCMI has developed in tandem with the field of BCI. As with BCI, in BCMI the notion of active control of a system is an important aspect (Miranda et al. 2005; Miranda et al. 2003). However, the notion of control in an artistic application can, and should, be approached with flexibility. There might be cases where a might want to avoid explicit control altogether. Nevertheless, in order to make progress, the science and engineering behind BCMI research should be aimed at the development of control methods as well as approaches for mapping EEG information into musical information. In practice, composers may of course choose to ignore all of these, depending on what they want to achieve. A number of low cost EEG equipment have been appearing in the market, most of which are commercialized in association with some sort of system for aiding meditation, relaxation, and so on. Whereas these have given musicians wider access to such technology, at the same time, however, pressures to manufacture them at low cost mean that the great majority of these systems fail to relay a reliable EEG signal for processing. This is an important fact we should all bear in mind, including those who are not so concerned with active control. Even in those cases where we might not wish to harness the EEG signal for explicit control of a music system, we do need a reliable EEG signal nevertheless. Otherwise we might end up making music with signals that are anything but the actual EEG. Therefore, the essential ingredients for making progress in the field of BCMI are: reliable hard- ware, powerful techniques for EEG signal processing, and creative methods for rendering the EEG signal into music. Guide to Brain-Computer Music Interfacing brings a number of chapters reporting on developments for the last two ingredients. This book emerged from a workshop on EEG and music composition that took place in 2011 at the University of Bordeaux, France, supported by the French Association for Musical Informatics (Association Française d’Informatique Musi- cale, AFIM). The workshop included presentations that were entirely technical, focusing on hardcore EEG analysis, and ones that focused on practical musical applications. This is reflected in this book, but in addition to chapters developed from papers presented at the workshop, we also commissioned chapters from experts on topics that were not covered by the workshop. We would like to thank all authors for their valuable contributions and Springer for the opportunity to publish this book.

Eduardo Reck Miranda Julien Castet Preface xiii

References

Knapp B, Lusted H (1996) Controlling computers with neural signals. Sci Am 275(4):82–87 Lucier A (1980) Chambers. Wesleyan University Press, Middletown, CT Miranda ER (2010) Plymouth brain-computer music interfacing project: from EEG audio mixers to composition informed by cognitive neuroscience. Int J Arts and Tech 3(2/3):154–176 Miranda ER, Roberts S, Stokes M (2005) On generating EEG for controlling musical systems. Biomed Tech 49(1):75–76 Miranda ER, Sharman K, Kilborn K, et al (2003) On Harnessing the Electroencephalogram for the Musical Braincap. Computer Music Journal 27(2):80–102 Rosenboom D (1990) The Performing Brain. Computer Music Journal 14(1):48–65 Teitelbaum R (1976) In Tune: Some Early Experiments in Biofeedback Music (1966–1974). In: Rosenboom D (ed) Biofeedback and the Arts: results of early experiments, Aesthetic Research Centre of Canada, Vancouver Contents

1 Brain–Computer Music Interfacing: Interdisciplinary Research at the Crossroads of Music, Science and Biomedical Engineering ...... 1 Eduardo Reck Miranda

2 Electroencephalogram-based Brain–Computer Interface: An Introduction ...... 29 Ramaswamy Palaniappan

3 Contemporary Approaches to Music BCI Using P300 Event Related Potentials ...... 43 Mick Grierson and Chris Kiefer

4 Prospective View on Sound Synthesis BCI Control in Light of Two Paradigms of Cognitive Neuroscience...... 61 Mitsuko Aramaki, Richard Kronland-Martinet, Sølvi Ystad, Jean-Arthur Micoulaud-Franchi and Jean Vion-Dury

5 Machine Learning to Identify Neural Correlates of Music and Emotions ...... 89 Ian Daly, Etienne B. Roesch, James Weaver and Slawomir J. Nasuto

6 Emotional Responses During Music Listening ...... 105 Konstantinos Trochidis and Emmanuel Bigand

7 A Tutorial on EEG Signal-processing Techniques for Mental-state Recognition in Brain–Computer Interfaces ..... 133 Fabien Lotte

8 An Introduction to EEG Source Analysis with an Illustration of a Study on Error-Related Potentials ...... 163 Marco Congedo, Sandra Rousseau and Christian Jutten

xv xvi Contents

9 Feature Extraction and Classification of EEG Signals. The Use of a Genetic Algorithm for an Application on Alertness Prediction ...... 191 Pierrick Legrand, Laurent Vézard, Marie Chavent, Frédérique Faïta-Aïnseba and Leonardo Trujillo

10 On Mapping EEG Information into Music...... 221 Joel Eaton and Eduardo Reck Miranda

11 Retroaction Between Music and Physiology: An Approach from the Point of View of Emotions...... 255 Pierre-Henri Vulliard, Joseph Larralde and Myriam Desainte-Catherine

12 Creative Music Neurotechnology with Symphony of Minds Listening ...... 271 Eduardo Reck Miranda, Dan Lloyd, Zoran Josipovic and Duncan Williams

13 Passive Brain–Computer Interfaces ...... 297 Laurent George and Anatole Lécuyer

Index ...... 309 Contributors

Marie Chavent IMB, UMR CNRS 5251, INRIA Bordeaux Sud-Ouest, University of Bordeaux, Bordeaux, France Mitsuko Aramaki Laboratoire de Mécanique et d’Acoustique (LMA), CNRS UPR 7051, Aix-Marseille University, Marseille, France Emmanuel Bigand Department of Cognitive Psychology, University of Bur- gundy, Dijon, France Marco Congedo GIPSA-lab, CNRS and Grenoble University, Grenoble, France Ian Daly School of Systems Engineering, University of Reading, Reading, UK Myriam Desainte-Catherine University of Bordeaux, Bordeaux, Talence, France; CNRS, LaBRI, UMR 5800, Bordeaux, Talence, France Joel Eaton Interdisciplinary Centre for Computer Music Research (ICCMR), Plymouth University, Plymouth, UK Frédérique Faïta-Aïnseba University of Bordeaux, Bordeaux, France Laurent George INRIA, Rennes, France Mick Grierson Embodied Audiovisual Interaction Group (EAVI), Goldsmiths Digital Studios, Department of Computing, Goldsmiths College, London, UK Zoran Josipovic Psychology Department, New York University, New York, NY, USA Christian Jutten GIPSA-lab, CNRS and Grenoble University, Grenoble, France Chris Kiefer Embodied Audiovisual Interaction Group (EAVI), Goldsmiths Digital Studios, Department of Computing, Goldsmiths College, London, UK Richard Kronland-Martinet Laboratoire de Mécanique et d’Acoustique (LMA), CNRS UPR 7051, Aix-Marseille University, Marseille, France Joseph Larralde University of Bordeaux, LaBRI, UMR 5800, Bordeaux, Talence, France Anatole Lécuyer INRIA, Rennes, France

xvii xviii Contributors

Pierrick Legrand IMB, UMR CNRS 5251, INRIA Bordeaux Sud-Ouest, University of Bordeaux, Bordeaux, France Dan Lloyd Department of Philosophy, Program in Neuroscience, Trinity College, Hartford, CT, USA Fabien Lotte Inria Bordeaux Sud-Ouest/LaBRI, Talence Cedex, France Jean-Arthur Micoulaud-Franchi Laboratoire de Neurosciences Cognitives (LNC), CNRS UMR 7291, Aix-Marseille University, Marseille, France Eduardo Reck Miranda Interdisciplinary Centre for Computer Music Research (ICCMR), Plymouth University, Plymouth, UK Slawomir J. Nasuto School of Systems Engineering, University of Reading, Reading, UK Ramaswamy Palaniappan Department of Engineering, School of Science and Engineering, University of Wolverhampton, Telford, UK Etienne B. Roesch School of Systems Engineering, University of Reading, Reading, UK Sandra Rousseau GIPSA-lab, CNRS and Grenoble University, Grenoble, France Konstantinos Trochidis Department of Cognitive Psychology, University of Burgundy, Dijon, France Leonardo Trujillo Instituto Tecnológico de Tijuana, Tijuana, BC, Mexico Laurent Vézard IMB, UMR CNRS 5251, INRIA Bordeaux Sud-Ouest, Univer- sity of Bordeaux, Bordeaux, France Jean Vion-Dury Laboratoire de Neurosciences Cognitives (LNC), CNRS UMR 7291, Aix-Marseille University, Marseille, France Pierre-Henri Vulliard University of Bordeaux, LaBRI, UMR 5800, Bordeaux, Talence, France James Weaver School of Systems Engineering, University of Reading, Reading, UK Duncan Williams Interdisciplinary Centre for Computer Music Research (IC- CMR), Plymouth University, Plymouth, UK Sølvi Ystad Laboratoire de Mécanique et d’Acoustique (LMA), CNRS UPR 7051, Aix-Marseille University, Marseille, France