# Dimensionality Reduction and Frequency Bin Optimization To Improve a Riemannian-based Classification Pipeline

Rune Frateur, Sylvain Chevallier, Florian Yger, Marie-Constance Corsi

► To cite this version:

Rune Frateur, Sylvain Chevallier, Florian Yger, Marie-Constance Corsi. Dimensionality Reduction and Frequency Bin Optimization To Improve a Riemannian-based Classification Pipeline. CORTICO 2023 - Journées COLlectif pour la Recherche Transdisciplinaire sur les Interfaces Cerveau-Ordinateur, May 2023, Paris, France. . hal-04140107

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Submitted on 24 Jun 2023

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# DIMENSIONALITY REDUCTION & FREQUENCY BIN OPTIMIZATION TO IMPROVE A RIEMANNIAN-BASED CLASSIFICATION PIPELINE

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ARAMIS
LAB
BRAIN DATA SCIENCE

Rune Frateur¹, Sylvain Chevallier², Florian Yger³, and Marie-Constance Corsi¹

1. Aramis project-team, Paris Brain Institute, France

3. LAMSADE, Univ Paris Dauphine PSL, France

2. TAU, LISN, Univ. Paris Saclay, France

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rune.frateur@student-cs.fr

## RIEMANNIAN GEOMETRY

This promising mathematical concept is applicable in the field of BCI as the **covariance matrices** lay on a Riemannian manifold, a curved space. This manifold enables to extract **structural information** that is embedded in the matrices. By mapping the matrices onto the **tangent space** (an Euclidean space), traditional classification methods can be applied, such as LDA and SVM.

**Subject variability** and **BCI illiteracy** pose a challenge to the BCI domain. To tackle this, the field has taken a turn towards **Riemannian geometry**, which has been shown to reach state-of-the-art performance. This promising mathematical concept is applicable in this field as the **covariance matrices** lay on a Riemannian manifold. This idea is exploited by combining Riemannian geometry with **functional connectivity** in a 'Functional Connectivity Ensemble' (FUCONE) in [1] with the aim to improve the performance of motor imagery (MI) BCI. FUCONE showed promising results, but this multi-step classification process also holds room for improvement. In this work, we focus on two aspects of the pipeline: **the choice of frequency band** and **dimensionality reduction** of the feature space.

## THE UPDATED 'FUCONE' PIPELINE

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## NEXT STEPS

- Introduce multi-modal data

- Take feature/subject bin-preferences into account

- More extensive bin/metric combinations

- Further hyperparameter tuning (focus on Riemannian PCA)

- Improve interpretability by analyzing the projection matrix

## REFERENCES

[1] M.-C. Corsi et al. "Functional Connectivity Ensemble Method to Enhance BCI Performance (FUCONE)". In: IEEE Trans. Biomed. Eng. 69.9 (2022), pp. 2826-2838. doi: 10.1109/TBME.2022.3154885.

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Paris Brain
Institute

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SORBONNE
UNIVERSITÉ

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Inserm