# Using critical dynamics to capture processes underlying Brain-Computer Interface performance

Marie-Constance Corsi, Pierpaolo Sorrentino, Denis P Schwartz, Nathalie George, Laurent Hugueville, Ari E Kahn, Sophie Dupont, Danielle S Bassett, Viktor Jirsa, Fabrizio de Vico Fallani

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To cite this version:

Marie-Constance Corsi, Pierpaolo Sorrentino, Denis P Schwartz, Nathalie George, Laurent Hugueville, et al.. Using critical dynamics to capture processes underlying Brain-Computer Interface performance. NeuroFrance 2023, Société des Neurosciences, May 2023, Lyon, France. hal-04140131

HAL Id: hal-04140131

https://inria.hal.science/hal-04140131

Submitted on 24 Jun 2023

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Distributed under a Creative Commons Attribution 4.0 International License
Using critical dynamics to capture processes underlying Brain-Computer Interface performance

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Marie-Constance Corsi*,
Inria-Paris Brain Institute, France

Pierpaolo Sorrentino*,
Institut de Neuroscience des Systèmes, France
What is a Brain-Computer Interface (BCI)?

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<figure><img src="image_5.png" /><figcaption>Control</figcaption></figure>

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## Behind the magic...

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## Non-invasive tools

EEG

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NIRS

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MEG

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fMRI

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Behind the magic...

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Behind the magic...

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Adapted from (Ahn & Jun, 2015)

## Problem:

Current BCIs fail to detect the mental intentions in ~30% of users – **BCI inefficiency** (Thompson, 2018)
⇒ Rely on local measurements of the brain activity
Neuronal avalanches: bursts of enhanced activity observed across neuroimaging modalities

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Adapted from [Polverino et al, Neurology, 2022]

Hypothesis:

The neuronal avalanches could spread differently according to the task & provide original markers of BCI performance.
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For a complete description of the protocol and the dataset, please refer to [Corsi et al, NeuroImage, 2020]

## Objective:

Tracking the dynamical features related to motor imagery as compared to rest
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Adapted from [Sorrentino et al, eLife, 2021]

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Neuronal avalanche

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Adapted from [Sorrentino et al, eLife, 2021]

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Avalanche Transition Matrix
(ATM)

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Subject-level

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Group-level

TASK-RELATED
RELIABLE EDGES

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Task-related differences are in edges hinging on pre/motor areas (in most subjects)
Subject-level

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Group-level

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Differences in transition probabilities discriminate mental states

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Differences in transition probabilities relate to BCI scores

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Transition probabilities as alternative features for BCI

MEG

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EEG

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signif

equals

ATM+SVM better

CSP+SVM better
Reliable functional information of task performance retrieval

* Meaningful information communication among regions on the large-scale & aperiodic and scale-free perturbation

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significant at **group** level</figcaption></figure>

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significant at **individual** level</figcaption></figure>

⇒ Tracking changes in perturbation spreading while performing different tasks via the avalanches transition matrices
Reliable functional information of task performance retrieval

* Meaningful information communication among regions on the large-scale & aperiodic and scale-free perturbation

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⇒ Focusing on higher-order perturbations to capture functionally-relevant processes & reliable information
Reliable functional information of task performance retrieval

Building innovative BCI protocols based on ATMs

* Outperforms the benchmark in EEG – gold standard in BCI → one step closer to the implementation

* Need to further investigate more suited classification methods

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Reliable functional information of task performance retrieval

Building innovative BCI protocols based on ATMs

Markers of BCI performance

- Current predictors of BCI

- Local measurements – power spectra (Ahn et al, 2015) → replicability issues

- Time-averaged brain interactions (Sugata et al, 2014) & Brain networks metrics [Gonzalez-Astudillo et al, JNE, 2020]

- Spreading of neuronal avalanches

- Patterns behaviorally meaningful (Chialvo et al, 2010)

- Computational fast marker

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Node strength [Corsi et al, NeuroImage 2020]
Multimodal core-periphery properties [Corsi et al, JNE 2021]
Paris Brain Institute

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Mario Chavez,
Denis Schwartz,
Nathalie George,
Laurent Hugueville,
Christophe Gitton
Sophie Dupont,

Juliana Gonzalez-Astudillo,
Fabrizio De Vico Fallani (PI)

Penn University

Ari E. Kahn,
Ankit Khambhati,
Jennifer Stiso,
Arnold Campbell,
Danielle S. Bassett (PI)

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Institut de Neuroscience
des Systèmes

Pierpaolo Sorrentino,

Viktor Jirsa (PI)
Interested in this study?

Scan the QR code to get access to the associated preprint!

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mccorsi/NeuronalAvalanches4BCI

Thank you for your attention!

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marie-constance.corsi@inria.fr

pierpaolo.sorrentino@univ-amu.fr

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MConstanceCorsi
PierpaSorre