Towards a Fully Interpretable EEG-based BCI System Fabien LOTTE1, Anatole LECUYER2, Cuntai GUAN1 1Institute for Infocomm Research (I2R), Singapore 2National

Research Institute for Computer Sciences and Control (INRIA), Rennes, France

Background and objectives o BCI research appears as a promising way to improve brain understanding o However, most BCI behave as “black boxes”, i.e., we cannot interpret what BCI algorithms automatically learnt from EEG data o The BCI community has stressed the need for BCI signal processing and classification techniques from which we could gain insights about the brain dynamics (McFarland06) o In this work, we propose a method to design a fully interpretable EEG-based BCI

Methods Our proposed BCI design combines interpretable features with an interpretable classifier Features: o

o

based on inverse solutions, i.e., methods able to reconstruct the activity in the whole brain volume, by using only scalp EEG signals We used the FuRIA feature extraction algorithm which can identify, thanks to an inverse solution, the most relevant brain regions and frequency bands to classify various mental states (Lotte09)

Classifier: o

Fuzzy Inference System, i.e., a classifier able to learn “If-Then” rules describing which input feature values correspond to which output class or mental state (Lotte07)

Linguistic Approximation: o

We also propose to rely on Zadeh’s “computing with words” framework (Zadeh96) to increase the system interpretability by using words instead of numbers

3-step workflow to learn an interpretable BCI system from EEG data

Results Evaluation on BCI competition II, data set IV, containing left or right hand finger movement intention EEG data o Our BCI, combining FuRIA and FIS, reached 85% of classification accuracy versus 84% for the competition winner o the automatically obtained rules, expressed with words, clearly explained what was expected by the literature, i.e., that hand movement intention is characterized by a decrease of brain activity in the motor cortex contralateral to the hand concerned, in the mu+beta frequency band (Pfurtscheller99)

Conclusion and discussion o We presented a method to design a fully interpretable BCI. This method relies on the combination of inverse-solutions, fuzzy inference systems and linguistic approximations. Our BCI can explain which activity in which brain regions and frequency bands corresponds to which mental state, thanks to rules expressed using simple words o A first evaluation suggested that the designed BCI actually reflected knowledge expected from the literature when used on movement intention EEG signals, and had high classification performances o This algorithm could be a useful tool to verify what the BCI learnt and to compare it with the literature on the brain signals analyzed. The rules being expressed with words, they could also prove useful to present the knowledge automatically extracted by the BCI to people not familiar with classification concepts, e.g., to medical doctors References (McFarland06) McFarland et al, IEEE Trans. on Neural Syst. And Rehab., 2006 (Lotte07) Lotte et al, IEEE Trans. on Neural Syst. and Rehab., 2007 (Lotte09) Lotte et al, IEEE Trans. on Sig. Proc., 2009 (Pfurtscheller99) Pfurtscheller and Lopez da Silva, , Clin. Neurophys., 1999 (Zadeh96) Zadeh, IEEE Trans. on Fuzzy Syst., 1996

Towards a Fully Interpretable EEG-based BCI System

2National Research Institute for Computer Sciences and Control (INRIA), Rennes, ... i.e., we cannot interpret what BCI algorithms automatically learnt from EEG data ... processing and classification techniques from which we could gain insights.

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