# Functional connectivity ensemble method to enhance BCI performance (FUCONE)

arXiv: https://arxiv.org/abs/2111.03122 · HTML: https://mlsanity.com/paper/2111.03122/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 83 references that are in the map.
- First posted on arXiv 4 Nov 2021.

## Builds on (most cited first, 6 of 6)

- [MOABB: Trustworthy algorithm benchmarking for BCIs](https://mlsanity.com/paper/1805.06427/) (2018)
- [Graph analysis of functional brain networks: practical issues in translational neuroscience](https://mlsanity.com/paper/1406.7391/) (2014)
- [Phase/amplitude synchronization of brain signals during motor imagery BCI tasks](https://mlsanity.com/paper/1912.02745/) (2019)
- [Network-based brain computer interfaces: principles and applications](https://mlsanity.com/paper/2006.13187/) (2020)
- [Improving J-divergence of brain connectivity states by graph Laplacian denoising](https://mlsanity.com/paper/2012.11240/) (2020)
- [BCI learning induces core-periphery reorganization in M/EEG multiplex brain networks](https://mlsanity.com/paper/2010.13459/) (2020)

## Built on by (most cited first, 6 of 6)

- [The largest EEG-based BCI reproducibility study for open science: the MOABB benchmark](https://mlsanity.com/paper/2404.15319/) (2024)
- [Classification of BCI-EEG based on augmented covariance matrix](https://mlsanity.com/paper/2302.04508/) (2023)
- [Pseudo-online framework for BCI evaluation: A MOABB perspective](https://mlsanity.com/paper/2308.11656/) (2023)
- [Geometric Neural Network based on Phase Space for BCI-EEG decoding](https://mlsanity.com/paper/2403.05645/) (2024)
- [Phase Synchrony Component Self-Organization in Brain Computer Interface](https://mlsanity.com/paper/2310.03748/) (2023)
- [LGL-BCI: A Motor-Imagery-Based Brain-Computer Interface with Geometric Learning](https://mlsanity.com/paper/2310.08051/) (2023)

## Abstract (arXiv)

Functional connectivity is a key approach to investigate oscillatory activities of the brain that provides important insights on the underlying dynamic of neuronal interactions and that is mostly applied for brain activity analysis. Building on the advances in information geometry for brain-computer interface, we propose a novel framework that combines functional connectivity estimators and covariance-based pipelines to classify mental states, such as motor imagery. A Riemannian classifier is trained for each estimator and an ensemble classifier combines the decisions in each feature space. A thorough assessment of the functional connectivity estimators is provided and the best performing pipeline, called FUCONE, is evaluated on different conditions and datasets. Using a meta-analysis to aggregate results across datasets, FUCONE performed significantly better than all state-of-the-art methods. The performance gain is mostly imputable to the improved diversity of the feature spaces, increasing the robustness of the ensemble classifier with respect to the inter- and intra-subject variability.