# Empirical evaluation on multiple BCI datasets of the functional connectivity ensemble (FUCONE) method

Marie-Constance Corsi, Sylvain Chevallier, Fabrizio de Vico Fallani, Florian Yger

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## To cite this version:

Marie-Constance Corsi, Sylvain Chevallier, Fabrizio de Vico Fallani, Florian Yger. Empirical evaluation on multiple BCI datasets of the functional connectivity ensemble (FUCONE) method. BCI 2023 - 10th International Brain-Computer Interface Meeting, BCI Society, Jun 2023, Bruxelles, Belgium. 10.3217/978-3-85125-962-9-24. [hal-04677809](https://hal.science/hal-04677809v1)

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# Empirical evaluation on multiple BCI datasets of the functional connectivity ensemble (FUCONE) method

M.-C. Corsi<fnref n="1" />*, S. Chevallier², F. De Vico Fallani<fnref n="1" />, F. Yger³,⁴

¹Sorbonne Université, Paris Brain Institute – ICM, CNRS, Inria, Inserm, AP-HP, Hôpital de la Pitié-Salpêtrière, Paris, France; ²LISN-CNRS, Université Paris-Saclay, Paris, France; ³LAMSADE, PSL, Université Paris Dauphine, CNRS, Paris, France; ⁴GREYC, ENSICAEN, CNRS, Caen, France

* E-mail: marie-constance.corsi@inria.fr

**Introduction** - Mastering Brain-Computer Interface (BCI) control via a voluntary modulation of the cerebral activity remains a learned skill that around 30 % of BCI users cannot develop control after completing a training program. Among the approaches adopted to circumvent this limitation is the design of more sophisticated classification algorithms to better discriminate the subjects' mental state [1]. Riemannian geometry-based methods are now the gold standard by notably improving the robustness of the performance [2]. One could consider the subjects' specificity by using alternative features that reflect the interconnected nature of brain activity. Functional Connectivity (FC), estimating the interaction between different brain areas, is a promising tool for BCI [3] relevant to discriminate subjects' mental states [4] and to study brain network reorganization underlying MI-based BCI training [5].

**Material, Methods and Results** - In this study, we propose a new framework that consists in combining functional connectivity estimators (namely Instantaneous and Imaginary coherences), Riemannian geometry, and ensemble learning. We compared our approach to the state-of-the-art methods (namely two pipelines relying on the use of the common spatial patterns [6], [7]; and a third one based on a purely riemannian method [8]). To assess the robustness and the replicability of our approach, we tested it through a large number of subjects, datasets, and motor imagery tasks. For a complete description of the pipeline<fnref n="1" /> and the datasets used in the replicability study one can refer to [6].

**Results** - FUCONE performed significantly better than all state-of-the-art methods in a meta-analysis that aggregated results across datasets. In the case of the classification of 2 classes (right hand vs feet), for four over five datasets, FUCONE showed the best results in terms of average accuracy from 0.82 to 0.91, and variability from +/-0.08 to +/-0.14 (see Figure 1). The performance gain is mostly imputable to the increased robustness of the ensemble classifier with respect to the inter- and intrasubject variability.

<figure><img src="image_3.png" /><figcaption>Figure 1 Replicability assessments and comparison with state-of-the-art pipelines. Analysis performed with 3-class (left hand vs right hand) datasets.</figcaption></figure>

**Discussion** - Even though our approach enabled a reliable improvement of the BCI accuracy, we still observed an important inter-subject variability. Several elements can explain it: the strong diversity in terms of MI tasks and of the number of channels considered without preprocessing, and the variety in the possible ways to detect neurophysiological properties underlying the MI performance. Preliminary results in the source space, relevant to provide a description of the mechanisms underlying the control of a BCI, tend to demonstrate an improvement of the performance and a reduction of the inter-subject variability of our approach with respect to the state-of-the-art methods.

**Significance:** Our results offer new insights into the need to consider the interconnected nature of brain functioning to improve the BCI performance.

**Acknowledgements:** Authors acknowledge support from the European Research Council (grant agreement No. 864729) and from the ANR as part of the "Investissements d'avenir" program (reference ANR-19-P3IA-0001).

## References

[1] F. Lotte et al., « A Review of Classification Algorithms for EEG-based Brain-Computer Interfaces: A 10-year Update », *J. Neural Eng.*, 2018.

[2] F. Yger, et al., « Riemannian Approaches in Brain-Computer Interfaces: A Review », *IEEE Trans. Neural Syst. Rehabil. Eng.*, 2017.

[3] J. Gonzalez-Astudillo, et al., « Network-based brain-computer interfaces: principles and applications », *J. Neural Eng.*, 2021.

[4] T. Cattai, et al., « Phase/Amplitude Synchronization of Brain Signals During Motor Imagery BCI Tasks », *IEEE Trans. Neural Syst. Rehabil. Eng.*, 2021.

[5] M.-C. Corsi et al., « Functional disconnection of associative cortical areas predicts performance during BCI training », *NeuroImage*, 2020.

[6] F. Lotte & C. Guan, « Regularizing Common Spatial Patterns to Improve BCI Designs: Unified Theory and New Algorithms », *IEEE Trans. Biomed. Eng.*, 2011.

[7] K. K. Ang, Z. et al., « Filter Bank Common Spatial Pattern (FBCSP) in Brain-Computer Interface », in *IJCNN conference*, 2008.

[8] A. Barachant, et al., "Classification of covariance matrices using a Riemannian-based kernel for BCI applications", *Neurocomputing*, 2013.

<footnote n="1">¹ The code used to perform the analysis are publicly available in this Github repository: https://github.com/mccorsi/FUCONE.git</footnote>