2015年07月23日　　12:15 〜 13:00

CiNet　1F　Conference Room

Fabien Lotte

Research Scientist , INRIA Bordeaux Sud-Ouest, France

担当: 　安藤 英由樹 （前田グループ）、Daniel Callan （PI）

Abstract:

Brain-Computer Interfaces (BCI) are systems that can translate the brain activity patterns of a user into messages or commands for an interactive application, this brain activity being usually measured using Electroencephalography (EEG). BCI technologies proved to be promising for a wide range of applications including communication and control for motor impaired users, gaming targeted at the general public, real-time mental state monitoring or stroke rehabilitation. Despite this promising potential, BCI are still scarcely used outside laboratories for practical applications. This is mainly due to the low reliability of current BCI.

In this talk, I will present our research towards addressing this limitation. First, I will present some EEG signal processing and classification algorithms we designed for making BCI more robust to noise, non-stationarities and to minimize calibration times. Then, I will show that making BCI more robust can also be achieved by making BCI users better at BCI control. Our method uses principles and guidelines from educational psychology and instructional design to enable BCI users to learn faster and more efficient BCI control. Finally, I will present how even not-so-reliable BCI technologies can still be useful for practical applications. For this, this talk will describe our work on neuroergonomics, i.e., on the use of brain signals to estimate passively the mental state of users (e.g., mental workload levels) during human-computer interaction, in order to assess the ergonomic qualities of this user interface.