# Exploring the Variability of Common Spatial Patterns in Motor Imagery Neurofeedback

Cassandra Dumas, Claire Dussard, Nathalie George, Marie-Constance Corsi

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Cassandra Dumas, Claire Dussard, Nathalie George, Marie-Constance Corsi. Exploring the Variability of Common Spatial Patterns in Motor Imagery Neurofeedback. Journées CORTICO 2025, May 2025, Lyon, France. hal-05078067

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Submitted on 21 May 2025

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# Exploring the Variability of Common Spatial Patterns in Motor Imagery Neurofeedback

(C. Dumas, C. Dussard, N. George, M.-C. Corsi)

CASSANDRA DUMAS – cassandra.dumas@icm-institute.org
# Neurofeedback: Principles & Limitations

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Cognitive strategy
learning

Visual or auditory
feedback

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Recording of
brain activity

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Real-time signal
processing

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Physiological
Feature Extraction

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Surface Laplacian

Personalization ?
# CSPs: Spatial Filters for EEG

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## How Are CSPs Used in Neurofeedback?

* A **single CSP** is selected to extract subject-specific motor-related activity and drive the feedback signal

* This filter acts as an **informed Laplacian**: spatial weights sum to zero, enhancing local sensorimotor contrasts

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## Known Limitations of CSPs

* CSP reliability is affected by **noise, artifacts**, and the **inherent variability of EEG data** [Yong et al, 2008]

* Difficulties in identifying a physiologically relevant pattern

* Lioi et al, 2020: fallback to **standard Laplacian** when CSP fails to capture expected motor activity (based on expert inspection)

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How can we ensure a CSP selection that is both reliable and physiologically meaningful?
# Study Design

**Participants** : 20 (10 ♀)

Age (mean ± sd) : 28.5 ± 6.8

Channels (10-20) : 32

Sampling rate : 1kHz

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**2 conditions**

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Raw data

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ICA-corrected data
# Expert-Based CSP Selection Procedure

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Question 1: Which CSP best reflects motor imagery-related activity?

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Question 2: How confident are you in your selection?

1. Not confident at all
2. 
3. 
4. 
5. Highly confident

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Choice analysis

* Cohen's kappa

* Grand average of selected CSPs

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Confidence analysis

* Wilcoxon signed-rank test
Manual CSP Selection – Raw Condition

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Cohen's Kappa : 0.63 (substantial agreement)
Manual CSP Selection – Raw Condition

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Manual CSP Selection – ICA Condition

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Cohen's Kappa : 0.81 (almost perfect agreement)
Manual CSP Selection – ICA Condition

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p=0.0506
# Automatic Selection: RSA

## Representational Similarity Analysis

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Euclidean
distance

Spearman correlation
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RSA

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Cohen's Kappa

● Expert 1 VS RSA : 0.11 (slight agreement)

● Expert 2 VS RSA : 0.02 (poor agreement)

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Cohen's Kappa

● Expert 1 VS RSA : 0.26 (fair agreement)

● Expert 2 VS RSA : 0.38 (fair agreement)
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## Beneficial effects of artifact removal

* Improved inter-expert agreement and **confidence**

* Average CSPs seem to be more similar and **physiologically congruent**

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## Limitations

* Expert selection remains the **most reliable and physiologically informed method**, but inter-expert variability exists

* RSA seems to detect relevant activity but shows **inconsistent performance across subjects**

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## Strengthening the robustness of component selection

* Increase the number of experts to enhance inter-rater reliability
  [Jing et al, 2023]

* Exploring **optimal transport** theory to support or automate selection processes
  [Chen et al, 2022]

* Restricting the analysis to the motor cortex
  [Lioi et al, 2020]

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## Assessing functional impact in NF

* Assessing CSP impact on **neurofeedback performance**
  [Lioi et al, 2020]
Thank you!

CASSANDRA DUMAS - cassandra.dumas@icm-institute.org