Lecturer: Léa Pillette
Fields: Human-Computer Interaction, Neurology, Computer Science
Content
This course offers a comprehensive introduction to the field of Brain-Computer Interfaces (BCIs), which are neurotechnologies that introduce promising possibilities to interact with digital devices solely through the acquisition and analysis of brain activity, measured in our case, as is common, using electroencephalography (EEG). For instance, BCIs can allow people to control the direction of a virtual character by imagining left- or right-hand movements. Despite their current lack of reliability, BCIs based on mental tasks hold promise for a wide range of clinical and non-clinical applications. The user training that these technologies require, during which people learn to control their own brain activity, is a significant limitation preventing a wider development of the technology. My research mostly focuses on improving this BCI user training, with different applications such as motor rehabilitation post-stroke.
During the first two sessions, we will survey the wide-ranging applications of BCI technology, from its essential role in assisting individuals with severe motor impairments to its emerging uses in entertainment, including gaming and virtual reality. We will also cover foundational principles of BCI operation, focusing on how brain activity is recorded and processed. You will learn the fundamentals of EEG signal acquisition alongside core signal processing and machine learning techniques used to decode user intent from neural activity. The third and fourth sessions will be hands-on, centered on brain-signal processing and BCI prototyping. In these sessions, students will process EEG signals recorded in real time from a volunteer wearing an EEG headset and build their first simple BCIs using OpenViBE, the open-source platform developed in our laboratory since 2008.
1. Session 1: will cover the basis of Brain-Computer Interfaces, basic concept and use
2. Session 2: will cover the basis of brain activity processing to create BCIs
3. Sessions 3 and 4: will provide hands-on experience on brain activity analysis and BCI
Literature
- Lotte, F., Bougrain, L., Cichocki, A., Clerc, M., Congedo, M., Rakotomamonjy, A., & Yger, F. (2018). A review of classification algorithms for EEG-based brain–computer interfaces: a 10 year update. Journal of neural engineering, 15(3), 031005.
- Roc, A., Pillette, L., Mladenovic, J., Benaroch, C., N’Kaoua, B., Jeunet, C., & Lotte, F. (2021). A review of user training methods in brain computer interfaces based on mental tasks. Journal of Neural Engineering, 18(1), 011002.
- Wolpaw, J. R. (2007). Brain–computer interfaces as new brain output pathways. The Journal of physiology, 579(3), 613-619.
Lecturer
Dr. Léa Pillette is a CNRS researcher and member of the Seamless team at IRISA, Rennes, France, since 2022. She obtained her PhD in computer science from the University of Bordeaux in 2019. Her research focuses on developing innovative methods to train individuals to regulate their brain activity, enabling more accessible and effective use of brain-computer interfaces for applications such as medical interventions and virtual world interactions.
Affiliation: CNRS
Homepage: https://lea-pillette.ovh/