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2. List of Contributors

Author

Nounagnon F. Agbangla, Atahan Agrali, Cédric T. Albinet, Awad Aljuaid, Guillaume Andéol, Jean M. André, Pietro Aricò, Branthomme Arnaud, Romain Artico, Michel Audiffren, Hasan Ayaz, Fabio Babiloni, Wendy Baccus, Carryl L. Baldwin, Hubert Banville, Klaus Bengler, Bruno Berberian, Jérémy Bergeron-Boucher, Ali Berkol, Pierre Besson, Siddharth Bhatt, Arianna Bichicchi, Martijn Bijlsma, Nikolai W.F. Bode, Vincent Bonnemains, Gianluca Borghini, Guillermo Borragán, Marc-André Bouchard, Angela Bovo, Eric Brangier, Anne-Marie Brouwer, Heinrich H. Bülthoff, Christopher Burns, Vincent Cabibel, Tuna E. Çakar, Daniel Callan, Aurélie Campagne, Travis Carlson, William D. Casebeer, Deniz Zengin Çelik, Cindy Chamberland, Caroline P.C. Chanel, Peter Chapman, Luc Chatty, Laurent Chaudron, Philippe Chevrel, Lewis L. Chuang, Caterina Cinel, Bernard Claverie, Antonia S. Conti, Yves Corson, Johnathan Crépeau, Adrian Curtin, Frédéric Dehais, Arnaud Delafontaine, Gaétane Deliens, Arnaud Delorme, Stefano I. Di Domenico, Gianluca Di Flumeri, Jean-Marc Diverrez, Manh-Cuong Do, Mengxi Dong, Andrew T. Duchowski, Anirban Dutta, Lydia Dyer, Sonia Em, Kate Ewing, Stephen Fairclough, Brian Falcone, Tiago H. Falk, Sara Feldman, Ying Xing Feng, Victor S. Finomore, Nina Flad, Alice Formwalt, Alexandra Fort, Paul Fourcade, Marc A. Fournier, Jérémy Frey, C. Gabaude, Olivier Gagey, Marc Garbey, Liliana Garcia, Thibault Gateau, Lukas Gehrke, Nancy Getchell, Evanthia Giagloglou, Christiane Glatz, Kimberly Goodyear, Robert J. Gougelet, Jonas Gouraud, Klaus Gramann, Dhruv Grewal, Carlos Guerrero-Mosquera, Céline Guillaume, Martin Hachet, Alain Hamaoui, Gabriella M. Hancock, Peter A. Hancock, Ahmad Fadzil M. Hani, Amanda E. Harwood, Mitsuhiro Hayashibe, Terry Heiman-Patterson, Girod Hervé, Maarten A.J. Hogervorst, Amy L. Holloway, Jean-Louis Honeine, Keum-Shik Hong, Klas Ihme, Kurtulus Izzetoglu, Meltem Izzetoglu, Philip L. Jackson, Christophe Jallais, Christian P. Janssen, Branislav Jeremic, Meike Jipp, Evelyn Jungnickel, Hélio Kadogami, Gozde Kara, Waldemar Karwowski, Quinn Kennedy, Theresa T. Kessler, Muhammad J. Khan, Rayyan A. Khan, Marius Klug, Amanda E. Kraft, Michael Krein, Ute Kreplin, Bartlomiej Kroczek, Lauens R. Krol, Frank Krueger, Ombeline Labaune, Daniel Lafond, Claudio Lantieri, Paola Lanzi, Amine Laouar, Dargent Lauren, Rachel Leproult, Véronique Lespinet-Najib, Ling-Yin Liang, Fabien Lotte, Ivan Macuzic, Nicolas Maille, Horia A Maior, S. Malin, Alexandre Marois, Franck Mars, Nicolas Martin, Nadine Matton, Magdalena Matyjek, Kevin McCarthy, Ryan McKendrick, Tom McWilliams, Bruce Mehler, Ranjana Mehta, Ranjana K. Mehta, Mathilde Menoret, Yoshihiro Miyake, Alexandre Moly, Rabia Murtza, Makii Muthalib, Mark Muthalib, Noman Naseer, Jordan Navarro, Roger Newport, Anton Nijholt, Michal Ociepka, Morellec Olivier, Ahmet Omurtag, Banu Onaral, Hiroki Ora, Bob Oudejans, Özgürol Öztürk, Martin Paczynski, Nico Pallamin, Raja Parasuraman, Mark Parent, René Patesson, Kou Paul, Philippe Peigneux, Matthias Peissner, G. Pepin, Stephane Perrey, Vsevolod Peysakhovich, Markus Plank, Riccardo Poli, Kathrin Pollmann, Simone Pozzi, Nancy M. Puccinelli, Jean Pylouster, Kerem Rızvanoğlu, Martin Ragot, Bryan Reimer, Emanuelle Reynaud, Joohyun Rhee, Jochem W. Rieger, Anthony J. Ries, Benoit Roberge-Vallières, Achala H. Rodrigo, Anne L. Roggeveen, Ricardo Ron-Angevin, Guillaume Roumy, Raphaëlle N. Roy, Anthony C. Ruocco, Bartlett A. Russell, Jon Russo, Richard M. Ryan, Amanda Sargent, Kelly Satterfield, Ben D. Sawyer, Sébastien Scannella, Menja Scheer, Melissa Scheldrup, Alex Schilder, Nicolina Sciaraffa, Lee Sciarini, Magdalena Senderecka, Sarah Sharples, Tyler H. Shaw, Patricia A. Shewokis, Andrea Simone, Hichem Slama, Alastair D. Smith, Bertille Somon, Hiba Souissi, Moritz Späth, Kimberly L. Stowers, Clara Suied, Junfeng Sun, Rajnesh Suri, Tong Boon Tang, Yingying Tang, Emre O. Tartan, Nadège Tebbache, Franck Techer, Cengiz Terzibas, Catherine Tessier, Claudine Teyssedre, Hayley Thair, Jean-Denis Thériault, Alexander Toet, Shanbao Tong, Jonathan Touryan, Amy Trask, Sébastien Tremblay, Anirudh Unni, François Vachon, Davide Valeriani, Benoît Valéry, Helma van den Berg, Valeria Vignali, Mathias Vukelić, Jijun Wang, Max L. Wilson, Emily Wusch, Petros Xanthopoulos, Eric Yiou, Amad Zafar, Thorsten O. Zander, Matthias D. Ziegler, and Ivana Živanovic-Macuzic

Published
2019
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3. Using Machine Learning Algorithms to Develop Adaptive Man–Machine Interfaces

Author

Kou Paul, Girod Herve, Branthomme Arnaud, Dargent Lauren, and Morellec Olivier

Subjects
Computer science, business.industry, Crew, ComputerApplications_COMPUTERSINOTHERSYSTEMS, Context (language use), Cognition, Workload, Machine learning, computer.software_genre, Phase (combat), Automation, Domain (software engineering), Variable (computer science), Artificial intelligence, business, Algorithm, computer
Abstract

Automation has been introduced in aircraft cockpits to reduce pilot workload and increase safety. However, a number of reports mention “human factors” issues and misunderstandings of an automated-system behavior or its displays by the crew as major contributors leading to flight incidents. The interfaces should play, nevertheless, a crucial role in improving man–machine cooperation, by displaying “the right information at the right time.” This need of adapted displays and interfaces is more important than ever as the missions are becoming more and more complex, especially in the military domain. Moreover, many factors in workload mitigation are identified as crew or mission dependent and are highly variable from one flight to another, such as the cognitive demands of the current phase of flight or mission situation, the pilot's experience or “airmanship,” or individual physiological parameters. Thus, we can think of an adaptive intelligent interface that would monitor the automated system–pilot team as well as the mission operational context to provide the correct display and controls to the user and enable better cooperation between the human operator and the machine to match the current demands of the operational situation. This paper aims to investigate the potential of machine learning algorithms to develop these adaptive intelligent interfaces.

Published
2018
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5. Highly sensitive index of cardiac autonomic control based on time-varying respiration derived from ECG.

Author

Gilfriche P, Arsac LM, Daviaux Y, Diaz-Pineda J, Miard B, Morellec O, and André JM

Subjects
Adolescent, Cognition, Humans, Male, Predictive Value of Tests, Reproducibility of Results, Stress, Psychological psychology, Time Factors, Young Adult, Athletes psychology, Electrocardiography, Heart innervation, Heart Rate, Physical Fitness, Respiration, Sedentary Behavior, Signal Processing, Computer-Assisted, Sympathetic Nervous System physiology, Vagus Nerve physiology
Abstract

Frequency-domain indices of heart rate variability (HRV) have been used as markers of sympathovagal balance. However, they have been shown to be degraded by interindividual or task-dependent variability, and especially variations in breathing frequency. The study introduces a method to analyze respiration-(vagally) mediated HRV, to better assess subtle variations in sympathovagal balance using ECG recordings. The method enhances HRV analysis by focusing the quantification of respiratory sinus arrhythmia (RSA) gain on the respiratory frequency. To this end, instantaneous respiratory frequency was obtained with ECG-derived respiration (EDR) and was used for variable frequency complex demodulation (VFCDM) of R-R intervals to extract RSA. The ability to detect cognitive stress in 27 subjects (athletes and nonathletes) was taken as a quality criterion to compare our method to other HRV analyses: Root mean square of successive differences, Fourier transform, wavelet transform, and scaling exponent. Three computer-based tasks from MATB-II were used to induce cognitive stress. Sympathovagal index (HF nu ) computed with our method better discriminates cognitive tasks from baseline, as indicated by P values and receiver operating characteristic curves. Here, transient decreases in respiratory frequency have shown to bias classical HRV indices, while only EDR-VFCDM consistently exhibits the expected decrease in the HF nu index with cognitive stress in both groups and all cognitive tasks. We conclude that EDR-VFCDM is robust against atypical respiratory profiles, which seems relevant to assess variations in mental demand. Given the variety of individual respiratory profiles reported especially in highly trained athletes and patients with chronic respiratory conditions, EDR-VFCDM could better perform in a wide range of applications.

Published
2018
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