# Characterization and Usability of Common Spatial Pattern Features in Motor Imagery Neurofeedback

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

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## To cite this version:

Cassandra Dumas, Claire Dussard, Nathalie George, Marie-Constance Corsi. Characterization and Usability of Common Spatial Pattern Features in Motor Imagery Neurofeedback. SPNC 2025 - Journées "Brain Insights into Memory and Consciousness" de la Société de Psychophysiologie et de Neurosciences Cognitives, Société de Psychophysiologie et de Neurosciences Cognitives, Nov 2025, Paris, France. hal-05366284

HAL Id: hal-05366284

https://hal.science/hal-05366284v1

Submitted on 14 Nov 2025

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Distributed under a Creative Commons Attribution 4.0 International License
# Characterization and Usability of Common Spatial Pattern Features in Motor Imagery Neurofeedback

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

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

<figure><img src="image_3.png" /></figure>

Recording of
brain activity

1

2

3

Cognitive strategy
learning

<figure><img src="image_4.png" /></figure>

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

<figure><img src="image_6.png" /></figure>

Real-time signal
processing

<figure><img src="image_7.png" /></figure>

Physiological
Feature Extraction

Visual or auditory
feedback
# Neurofeedback: Principles & Limitations

<figure><img src="image_8.png" /></figure>

<figure><img src="image_9.png" /></figure>

Physiological
Feature Extraction

<figure><img src="image_10.png" /></figure>

Visual or auditory
feedback

<figure><img src="image_11.png" /></figure>

Cognitive strategy
learning

<figure><img src="image_12.png" /></figure>

Recording of
brain activity

<figure><img src="image_13.png" /></figure>

Real-time signal
processing
# Neurofeedback: Principles & Limitations

<figure><img src="image_14.png" /></figure>

Cognitive strategy
learning

Visual or auditory
feedback

<figure><img src="image_15.png" /></figure>

Recording of
brain activity

<figure><img src="image_16.png" /></figure>

Real-time signal
processing

<figure><img src="image_17.png" /></figure>

Physiological
Feature Extraction

Right Hand Motor
Imagery (8-30Hz)

<figure><img src="image_18.png" /></figure>

<figure><img src="image_19.png" /></figure>

Surface Laplacian
# Neurofeedback: Principles & Limitations

<figure><img src="image_20.png" /></figure>

Cognitive strategy
learning

Recording of
brain activity

<figure><img src="image_21.png" /></figure>

Real-time signal
processing

<figure><img src="image_22.png" /></figure>

Visual or auditory
feedback

<figure><img src="image_23.png" /></figure>

Physiological
Feature Extraction

Right Hand Motor
Imagery (8-30Hz)

<figure><img src="image_24.png" /></figure>

<figure><img src="image_25.png" /></figure>

Surface Laplacian

Personalization ?
# CSPs: Spatial Filters for EEG

<figure><img src="image_26.png" /></figure>

Use CSPs to extract subject-specific spatial filters
# CSPs: Spatial Filters for EEG

<figure><img src="image_27.png" /></figure>

Use CSPs to extract subject-specific spatial filters

<figure><img src="image_28.png" /></figure>
# CSPs: Spatial Filters for EEG

<figure><img src="image_29.png" /></figure>

Use CSPs to extract subject-specific spatial filters

<figure><img src="image_30.png" /></figure>

<figure><img src="image_31.png" /></figure>
# CSP in neurofeedback

<figure><img src="image_32.png" /></figure>

## 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
# CSP in neurofeedback

<figure><img src="image_33.png" /></figure>

## 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

<figure><img src="image_34.png" /></figure>

## 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)
# CSP in neurofeedback

<figure><img src="image_35.png" /></figure>

## 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

<figure><img src="image_36.png" /></figure>

## 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)

<figure><img src="image_37.png" /></figure>

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

<figure><img src="image_38.png" /></figure>
# Study Design

Participants : 20 (10 ♀)

Age (mean ± sd) : 28.5 ± 6.8

Channels (10-20) : 32

Sampling rate : 1kHz

<figure><img src="image_39.png" /></figure>

<figure><img src="image_40.png" /></figure>
# Study Design

Participants : 20 (10 ♀)

Age (mean ± sd) : 28.5 ± 6.8

Channels (10-20) : 32

Sampling rate : 1kHz

<figure><img src="image_41.png" /></figure>

<figure><img src="image_42.png" /></figure>

**2 conditions**

<figure><img src="image_43.png" /></figure>

Raw data

<figure><img src="image_44.png" /></figure>

ICA-corrected data
# Expert-Based CSP Selection Procedure

<figure><img src="image_45.png" /></figure>

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

<figure><img src="image_46.png" /></figure>

**Question 2: How confident are you in your selection?**

1. Not confident at all
2. High confidence
3. Confident
4. Somewhat confident
5. Highly confident
# Expert-Based CSP Selection Procedure

<figure><img src="image_47.png" /></figure>

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

<figure><img src="image_48.png" /></figure>

**Question 2: How confident are you in your selection?**

1. Not confident at all
2. High confidence
3. Somewhat confident
4. Confident
5. Highly confident

<figure><img src="image_49.png" /></figure>

## Standard CSP Selection

<figure><img src="image_50.png" /></figure>

Maximize Contrast
between Conditions

<figure><img src="image_51.png" /></figure>

Minimize Variance
during MI
# Expert-Based CSP Selection Procedure

<figure><img src="image_52.png" /></figure>

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

<figure><img src="image_53.png" /></figure>

**Question 2: How confident are you in your selection?**

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

<figure><img src="image_54.png" /></figure>

Standard CSP Selection

<figure><img src="image_55.png" /></figure>

Maximize Contrast
between Conditions

<figure><img src="image_56.png" /></figure>

Minimize Variance
during MI

<figure><img src="image_57.png" /></figure>

<figure><img src="image_58.png" /></figure>

Analysis

➤ Cohen's kappa

➤ Grand average of selected CSPs
# Manual CSP Selection

Cohen's Kappa:
0.63

<figure><img src="image_59.png" /></figure>

<figure><img src="image_60.png" /></figure>

Cohen's Kappa:
0.81

<figure><img src="image_61.png" /></figure>

<figure><img src="image_62.png" /></figure>

<figure><img src="image_63.png" /></figure>
# Automatic Selection: Contrast VS Variance

<figure><img src="image_64.png" /></figure>

## Maximize Contrast between Conditions

<figure><img src="image_65.png" /></figure>

Highlights components that best
discriminate between states

<figure><img src="image_66.png" /></figure>

<figure><img src="image_67.png" /></figure>

<figure><img src="image_68.png" /></figure>

<figure><img src="image_69.png" /></figure>

<figure><img src="image_70.png" /></figure>
# Automatic Selection: Contrast VS Variance

<figure><img src="image_71.png" /></figure>

## Maximize Contrast between Conditions

* Highlights components that best discriminate between states

<figure><img src="image_72.png" /></figure>

## Minimize Variance during MI

* Identifies stable and consistent patterns within the MI condition

<figure><img src="image_73.png" /></figure>

<figure><img src="image_74.png" /></figure>

<figure><img src="image_75.png" /></figure>

<figure><img src="image_76.png" /></figure>

<figure><img src="image_77.png" /></figure>

<figure><img src="image_78.png" /></figure>

<figure><img src="image_79.png" /></figure>
# Automatic Selection: Contrast VS Variance

<figure><img src="image_80.png" /></figure>

## Maximize Contrast between Conditions

* Highlights components that best discriminate between states

<figure><img src="image_81.png" /></figure>

<figure><img src="image_82.png" /></figure>

<figure><img src="image_83.png" /></figure>

<figure><img src="image_84.png" /></figure>

## Minimize Variance during MI

* Identifies stable and consistent patterns within the MI condition

<figure><img src="image_85.png" /></figure>

<figure><img src="image_86.png" /></figure>
# Automatic Selection: Contrast VS Variance

<figure><img src="image_87.png" /></figure>

## Maximize Contrast between Conditions

* Highlights components that best discriminate between states

<figure><img src="image_88.png" /></figure>

<figure><img src="image_89.png" /></figure>

## Minimize Variance during MI

* Identifies stable and consistent patterns within the MI condition

<figure><img src="image_90.png" /></figure>

<figure><img src="image_91.png" /></figure>

<figure><img src="image_92.png" /></figure>

<figure><img src="image_93.png" /></figure>
# Neurofeedback Performance

<figure><img src="image_94.png" /></figure>
# Neurofeedback Performance

<figure><img src="image_95.png" /></figure>

Virtual hand (HAND)

**Neurofeedback Performance:**

Median power in 8-30Hz band on a
Laplacian C3 relative to reference level

<figure><img src="image_96.png" /></figure>

<figure><img src="image_97.png" /></figure>
Discussion & Perspectives

<figure><img src="image_98.png" /></figure>

Beneficial effects of artifact removal

* Improved inter-expert agreement and confidence

* Average CSPs seems to be more similar and physiologically congruent
<figure><img src="image_99.png" /></figure>

## Beneficial effects of artifact removal

* Improved inter-expert agreement and confidence

* Average CSPs seem to be more similar and physiologically congruent

<figure><img src="image_100.png" /></figure>

## Limitations

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

* Best contrasts CSP seems to detect relevant activity but shows **variability across subjects**
<figure><img src="image_101.png" /></figure>

## Beneficial effects of artifact removal

* Improved inter-expert agreement and confidence

* Average CSPs seem to be more similar and physiologically congruent

<figure><img src="image_102.png" /></figure>

## Limitations

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

* Best contrasts CSP seems to detect relevant activity but shows variability across subjects

<figure><img src="image_103.png" /></figure>

## Strengthening the robustness of component selection

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

* Restricting the analysis to the **motor cortex**
  [Lioi et al, 2020]
<figure><img src="image_104.png" /></figure>

## Beneficial effects of artifact removal

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

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

<figure><img src="image_105.png" /></figure>

## Limitations

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

* Best contrasts CSP seems to detect relevant activity but shows **variability across subjects**

<figure><img src="image_106.png" /></figure>

## Strengthening the robustness of component selection

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

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

<figure><img src="image_107.png" /></figure>

## Assessing CSP feature variability

* **Robustness** and **Stability** of the feature
[Park et al, 2013]

* Relevance for clinical neurofeedback adaptation
[Lioi et al, 2020]
<figure><img src="image_108.png" /></figure>

Paris Brain
Institute

<figure><img src="image_109.png" /></figure>

SORBONNE
UNIVERSITÉ

<figure><img src="image_110.png" /></figure>

ed3c

<figure><img src="image_111.png" /></figure>

<figure><img src="image_112.png" /></figure>

Thank you!

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

<figure><img src="image_113.png" /><figcaption>Claire Dussard</figcaption></figure>

<figure><img src="image_114.png" /><figcaption>Nathalie George</figcaption></figure>

<figure><img src="image_115.png" /><figcaption>Marie-Constance
Corsi</figcaption></figure>