Section: New Results
EEG signal processing
Participants : Alison Cellard, Nicoletta Caramia, Fabien Lotte.
Spatial filters are powerful tools for EEG classification for BCI design, able to reduce spatial blurring effects. In particular, optimal spatial filters have been designed to classify EEG signals based on band power features. Unfortunately, there are other relevant EEG features for which no optimal spatial filter exists. This is the case for Phase Locking Value (PLV) features, which measure the synchronization between 2 EEG channels. Therefore, we proposed to create such a pair of optimal spatial filters for PLV-features  . To do so, we optimized a functional measuring the discriminability of PLV-features based on a genetic algorithm. An evaluation of our algorithm on a motor imagery EEG data set showed that using optimized spatial filters led to higher classification performances, and that combining the resulting PLV features with traditional methods boosts the overall BCI performances.
We also wrote a chapter that is an introductory overview and a tutorial of signal processing techniques that can be used to recognize mental states from EEG signals in BCI  . More particularly, this chapter presented how to extract relevant and robust spectral, spatial and temporal information from noisy EEG signals (e.g., Band Power features, spatial filters such as Common Spatial Patterns or xDAWN, etc.), as well as a few classification algorithms (e.g., Linear Discriminant Analysis) used to classify this information into a class of mental state. It also briefly touched on alternative, but currently less used approaches.