Miccai 2020 Tutorial
BiCoIN-4-CAI : Brain Computer Interface and Neurofeedback, a new era of Medical Image Intervention
The attention of the scientific community to Brain Computer Interface (BCI) and Neurofeedback (NF) is rapidly increasing and the variety of methodologies and approaches used in NF/BCI has consequently proliferated in the last years. This comes with the necessity to adapt well established imaging techniques to the challenges of NF/BCI (i.e. real-time implementations, adequacy of experimental protocols, appropriate choice of control groups, etc.). We believe It is timely for the Miccai community to introduce this new field of research through a Tutorial session at Miccai 2020. This represents the ideal context to give a comprehensive, didactic overview of the field, to provide tools and orient researchers towards appropriate methodological choices for specific clinical applications. The Tutorial session will be a promising moment of exchange, critical discussion of current limitations and significant contribution to the growth and cooperation of the Neurofeedback/BCI community.
- Christian BARILLOT, IRISA, Rennes, France (Christian.Barillot@irisa.fr)
- Ranganatha SITARAM, Pontificia Universidad Católica de Chile, Chile (firstname.lastname@example.org)
Team from Empenn U1228 / Inria, Rennes, France:
- Christian Barillot, DR CNRS, IRISA, Rennes, France
- Mathis Fleury, PhD, Inria, Rennes, France
- Anatole Lecuyer, DR Inria, Rennes, France
- Giulia Lioi, Research Fellow, Inria Rennes, France
- Pierre Maurel, Assistant Professor, Univ. Rennes, France
Team from Santiago, Chile: Ranganatha SITARAM, Prof.
There is a growing interest in Neurofeedback (NF) and Brain Computer Interface (BCI for their potentials as a clinical interventional tool. While the majority of NF/BCI applications have historically relied on EEG (which is the only modality used in the clinical practice to date), in the last years a variety of brain imaging techniques have been used in NF, and for an expanding body of clinical applications. Acquiring a critical knowledge of the different methodologies and their limitations is fundamental to better orient the choice of the approaches, protocols and tools for specific clinical applications. The aim of this tutorial is to provide an introduction to NF training and BCI control and a comprehensive overview of the current state of the art of NF/BCI methods and most relevant clinical targets. The course will cover technical and experimental aspects of the different imaging modalities (EEG, fMRI, fNIRS and MEG) and approaches (activity and connectivity-based) used in NF/BCI, and discuss their potential applications and limitations for developing a new era of Medical Image-based Interventions. A round table will be devoted to debate on the respective benefits of the different sensing approaches and how they can become complementary in future brain imaging research to increase specificity and efficacy of NF/BCI.
Neurofeedback (NF) and brain-computer interface (BCI) provide an individual with real-time bio-control of their brain activity and allow them to regulate their own brain functions by providing real-time sensory feedback of the brain “in action”. Brain activity can be measured using various non-invasive sensors, such as electro- encephalography (EEG) and magneto-encephalography (MEG) for direct neuronal activity, and functional magnetic resonance imaging (fMRI) or near-infrared spectroscopy (NIRS) for measuring related hemodynamics. Although EEG is currently the more common modality used by NF/BCI clinical practitioners, it often lacks of specificity due to its low spatial resolution. Research has therefore recently turned to explore the other brain functional modalities that target the activity of different regions of the brain more precisely. Though promising, current NF/BCI technologies suffer from the limitations of the technology, by providing either low spatial or temporal resolutions according to the sensor that is used. The future belongs to combine sensors in order to get the best of the different technologies. The Tutorial will address the sum of technological, computational challenges and potential clinical applications that can be expected in the future from this emerging technological evolution.
Tentative Program (will be updated)
- Ranganatha SITARAM, PhD, Pontificia Universidad Católica de Chile, Chile
- Title : Introduction to Brain Computer Interface and Neurofeedback for Medical Image Intervention
- Website: https://ingenieriabiologicaymedica.uc.cl/en/people/faculty/175-ranganatha-sitaram
- Christian BARILLOT, CNRS Research Director, IRISA, Rennes, FR
- Title : Multimodal neurofeedback for brain rehabilitation
- Website: http://people.irisa.fr/Christian.Barillot/
- Pierre MAUREL, Assistant Professor, Univ. Rennes, IRISA, Rennes, FR
- Title : Learning EEG-Informed fMRI Model for Hybrid EEG-fMRI Neurofeedback Prediction
- Website: http://www.normalesup.org/~pmaurel/
- David E. LINDEN, Scientific Director, School for Mental Health and Neuroscience, FHML, Maastricht University, NL
- Title: Clinical applications of (fMRI-)neurofeedback in psychiatry and neurorehabilitation
- Publications: https://scholar.google.co.uk/citations?hl=en&user=vSHrbPMAAAAJ.
- Fabien LOTTE, Inria Research Director, Inria, Bordeaux, FR
- Title: Brain-Computer Interfaces in Medical control systems
- Website: https://sites.google.com/site/fabienlotte/
- Talma HENDLER, MD-PhD, Professor of Psychiatry and Neuroscience, Tel Aviv University, director of the Sagol Brain Institute Tel-Aviv, IL
- Title: Electrical fingerprint (EFP) neurofeedback training
- Website: https://www.cbf-tlv.com/talma-hendler