simBCI—A Framework for Studying BCI Methods by Simulated EEG

Jussi Lindgren (1) , Adrien Merlini (2) , Anatole Lécuyer (1) , Francesco Andriulli (3)

1 Hybrid - 3D interaction with virtual environments using body and mind
2 Lab-STICC - Laboratoire des sciences et techniques de l'information, de la communication et de la connaissance
3 Lab-STICC_ENIB_CID_TOMS

Brain-computer interface (BCI) methods are commonly studied using electroencephalogram (EEG) data recorded from human experiments. For understanding and developing BCI signal processing techniques, real data is costly to obtain and its composition is a priori unknown. The brain mechanisms generating the EEG are not directly observable and their states cannot be uniquely identified from the EEG. Subsequently, we do not have generative ground truth for real data. In this paper, we propose a novel convenience framework called simBCI to alleviate testing and studying BCI signal processing methods in simulated, controlled conditions. The framework can be used to generate artificial BCI data and to test classification pipelines with such data. Models and parameters on both data generation and the signal processing side can be iterated over to examine the interplay of different combinations. The framework provides the first time open source implementations of several models and methods. We invite researchers to insert more advanced models. The proposed system does not intend to replace human experiments. Instead, it can be used to discover hypotheses, study algorithms, educate about BCI, and debug signal processing pipelines of other BCI systems. The proposed framework is modular, extensible, and freely available as open source. 1 It currently requires MATLAB.

Domaines: Synthèse d'image et réalité virtuelle [cs.GR]; Neurosciences; Imagerie médicale; Traitement du signal et de l'image [eess.SP]

Jussi Lindgren, Adrien Merlini, Anatole Lécuyer, Francesco Andriulli. simBCI—A Framework for Studying BCI Methods by Simulated EEG. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2018, 26 (11), pp.2096-2105. ⟨10.1109/TNSRE.2018.2873061⟩. ⟨hal-01953341⟩