Information détaillée concernant le cours
EEG II - EEG Methods: An overview
8th – 10th November 2021
Dr. Jérôme Barral, Prof. Catherine Brandner, Dr. Paolo Ruggeri, Dr. Etienne Sallard - UNIL
Dr. Jean-Francois Knebel, Biospectral SA, Lausanne
Dr. Mahmoud Hassan, MINDig, Rennes, France & School of Engineering, Reykjavik University, Iceland
Dr. David Pascucci, Brain Mind Institute, EPFL
Dr. Tomas Ros, CIBM, UNIGE
This course is for PhD students focusing research on EEG in healthy and clinical population. Invited speakers and hands-on sessions with training on EEG analysis are planned over three days. The main goal is to develop new knowledge or increase understanding about methods used in EEG.
Monday, November 8th
Tuesday, November 9th
Wednesday, November 10th
The self-tuning brain: control and plasticity mechanisms of neurofeedback
Tomas Ros, CIBM, University of Geneva, Switzerland
Tuesday, November 9th, 09h30-11h30. University of Lausanne
Neurofeedback provides the possibility of endogenously manipulating brain activity as an independent variable, and as such provides a novel way to investigate brain function and neuroplasticity. This seminar will introduce how EEG-based neurofeedback may be deployed in novel experimental and clinical paradigms using multimodal windows on the brain, including fMRI and transcranial magnetic stimulation. Here, the neuroplastic effects of neurofeedback will be examined from the theoretical perspectives of Hebbian and homeostatic models of brain plasticity. Finally, the seminar will feature a live demonstration -including signal processing pipeline- of alpha oscillation neurofeedback, where participants will be able to subjectively experience what it is like to control their own brain activity.
Modeling large-scale dynamic brain networks during perception and cognition
David Pascucci, Brain Mind Institute, Ecole Polytechnique Fédérale de Lausanne (EPFL), Switzerland
Wednesday, November 10th, 09h-10h15. University of Lausanne
In the last decade, the rising field of network neuroscience has emphasized the need for advanced functional connectivity measures. A major goal is to understand the dynamics of directed and large-scale neuronal interactions that underlie perception, cognition, and action. Modeling network interactions that evolve at the sub-second timescale of brain functions, however, remains a major ongoing challenge. Here, I will describe an endeavor to characterize fast dynamics in functional brain networks during evoked brain activity. I will present an extension of classical linear adaptive filters for modeling event-related changes in directed connectivity patterns, using electroencephalography and source imaging data. Within this modeling framework, I will then evaluate the advantages of combining structural and functional connectivity, under a multimodal imaging scheme. After introducing the methods, I will review recent results of their application in the field of human perception and attention, focusing on how accurate models of time-varying brain connectivity could yield new fundamental insights into the dynamic and frequency-specific computations behind cognition and behavior.
Dynamics of electrophysiological brain networks
Mahmoud Hassan MINDig, Rennes, France & School of Engineering, Reykjavik University, Iceland
Wednesday, November 10th, 10h30-11h45. University of Lausanne
The human brain is a dynamic electrical complex network. Electro/magneto-encephalography (EEG/MEG) signals provide a unique direct and noninvasive access to the electrophysiological activity of the entire brain, at the millisecond. During this talk, I will introduce the methods used to analyze EEG/MEG functional network dynamics using computational modeling (Allouch et al., 2021) and real data acquired during task-free and task-related paradigms. I will focus on the network reconfiguration at sub-second time scale during different behavioral tasks (motor, visual and memory). Methodological issues from signal preprocessing to network estimation and clustering into functional states will be also discussed (Tabbal et al., 2021).
Allouch, S., Yochum, M., Kabbara, A., Duprez, J., Khalil, M., Wendling, F., Hassan, M., Modolo, J., 2021. Mean-field modeling of brain-scale dynamics for the evaluation of EEG source-space networks. Brain Topogr. 1–12.
Tabbal, J., Kabbara, A., Khalil, M., Benquet, P., Hassan, M., 2021. Dynamics of task-related electrophysiological networks: a benchmarking study. NeuroImage 117829.
UNIL, rooms to be assigned
Writing report: analyze a set of EEG data with a method learnt during the workshop. Deadline: 01 December 2021
Information for the hands-on sessions
You will use your own computer for the practice part. Matlab and the following software are required:
We will provide more detailed information to all the participants by Monday November 1st.