Considering user training and alternative applications to design Usable EEG-based BCI Technologies
Event details
| Date | 22.03.2016 |
| Hour | 10:30 › 11:30 |
| Speaker |
Dr. Fabien Lotte, Inria Bordeaux Sud-Ouest/LaBRI Bio: Fabien LOTTE obtained a M.Sc., a M.Eng. and a PhD degree in computer sciences, all from the National Institute of Applied Sciences (INSA) Rennes, France, in 2005 and 2008 respectively. As a PhD candidate he was part of the BUNRAKU team at the Inria Rennes Bretagne-Atlantique and member of the OpenViBE project dedicated to brain-computer interfaces and virtual reality. He was supervised by Dr. Anatole LECUYER and Pr. Bruno ARNALDI. His PhD Thesis received both the PhD Thesis award 2009 from AFRIF (French Association for Pattern Recognition) and the PhD Thesis award 2009 accessit (2nd prize) from ASTI (French Association for Information Sciences and Technologies). In 2009 and 2010, he was a research fellow at the Institute for Infocomm Research (I2R) in Singapore, working in the Brain-Computer Interface Laboratory led by Dr. Cuntai GUAN. Since January 2011, he is a Research Scientist (with tenure) at Inria Bordeaux Sud-Ouest, France, in team Potioc (http://team.inria.fr/potioc/). His research interests include brain-computer interfaces, virtual reality and 3D interaction, pattern recognition and signal processing. |
| Location | |
| Category | Conferences - Seminars |
Brain-Computer Interfaces (BCIs) are systems that can translate brain activity patterns of a user - typically measured using Electroencephalography (EEG) - into messages or commands for an interactive application.
Although EEG-based BCIs have proven promising for a wide range of applications they are still scarcely used outside laboratories for practical applications. The main reason preventing EEG-based BCIs from being widely used is arguably their poor usability, which is notably due to their low reliability and long training times. A significant research effort has been targeted at the EEG signal processing level to address these issues.
However, in this talk I will present our work that aims at making EEG-based BCIs usable, i.e., at increasing their efficacy and efficiency, targeted at two other levels:
1) at the user training level, to ensure that users can learn to control a BCI efficiently and effectively, and
2) at the usage level, to explore novel applications of BCIs for which the current reliability can already be useful.
First, we advocate that BCIs can be made more usable by guiding users to efficiently learn BCI control mastery. Indeed, BCI control is known to be a skill that needs to be learnt. A study of models and guidelines from educational sciences enabled us to identify many theoretical limitations of current standard BCI training approaches, thus highlighting the need for alternative ones. In particular, educational sciences recommend to train people with adapted and adaptive training tasks, using explanatory feedback in motivating environments. In contrast standard BCI training protocols are commonly fixed, repetitive, rather boring and provide purely corrective feedback. To address these limitations, we studied what kind of users manage to use a BCI and why. We also explored new feedback types, e.g., multi-user feedback and tactile feedback to help users to learn BCI control skills more efficiently.
Second, BCIs can be made more usable by being used for other applications than communication and control. To this end, we notably explored the use of BCIs for neuroergonomics, i.e., using brain signals to passively estimate some of the relevant user's mental states during human-computer interaction, in order to assess the ergonomic qualities of this interface. In particular, we showed that one can estimate mental workload during complex 3D manipulation and navigation
tasks in order to assess or compare interaction techniques and devices. We have also been able to study stereoscopic displays by estimating visual comfort in EEG signals. Another usage of BCIs, that we found promising and useful, is real-time brain activity and mental state visualization. We designed a number of devices based on augmented reality and/or tangible interfaces to enable novice users to visualize their own brain activity or mental states in real-time, with potential applications in fields as wide and diverse as education, self-awareness or well-being.
Although EEG-based BCIs have proven promising for a wide range of applications they are still scarcely used outside laboratories for practical applications. The main reason preventing EEG-based BCIs from being widely used is arguably their poor usability, which is notably due to their low reliability and long training times. A significant research effort has been targeted at the EEG signal processing level to address these issues.
However, in this talk I will present our work that aims at making EEG-based BCIs usable, i.e., at increasing their efficacy and efficiency, targeted at two other levels:
1) at the user training level, to ensure that users can learn to control a BCI efficiently and effectively, and
2) at the usage level, to explore novel applications of BCIs for which the current reliability can already be useful.
First, we advocate that BCIs can be made more usable by guiding users to efficiently learn BCI control mastery. Indeed, BCI control is known to be a skill that needs to be learnt. A study of models and guidelines from educational sciences enabled us to identify many theoretical limitations of current standard BCI training approaches, thus highlighting the need for alternative ones. In particular, educational sciences recommend to train people with adapted and adaptive training tasks, using explanatory feedback in motivating environments. In contrast standard BCI training protocols are commonly fixed, repetitive, rather boring and provide purely corrective feedback. To address these limitations, we studied what kind of users manage to use a BCI and why. We also explored new feedback types, e.g., multi-user feedback and tactile feedback to help users to learn BCI control skills more efficiently.
Second, BCIs can be made more usable by being used for other applications than communication and control. To this end, we notably explored the use of BCIs for neuroergonomics, i.e., using brain signals to passively estimate some of the relevant user's mental states during human-computer interaction, in order to assess the ergonomic qualities of this interface. In particular, we showed that one can estimate mental workload during complex 3D manipulation and navigation
tasks in order to assess or compare interaction techniques and devices. We have also been able to study stereoscopic displays by estimating visual comfort in EEG signals. Another usage of BCIs, that we found promising and useful, is real-time brain activity and mental state visualization. We designed a number of devices based on augmented reality and/or tangible interfaces to enable novice users to visualize their own brain activity or mental states in real-time, with potential applications in fields as wide and diverse as education, self-awareness or well-being.
Practical information
- Informed public
- Free
Organizer
- Prof. José del R. Millán
Contact
- Catherine Wannier