# Challenges in Common Spatial Pattern Reliability for Neurofeedback

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

► To cite this version:

Cassandra Dumas, Claire Dussard, Nathalie George, Marie-Constance Corsi. Challenges in Common Spatial Pattern Reliability for Neurofeedback. BCI 2025 - 11th International BCI Meeting, Jun 2025, Banff, Canada. pp.1-1, 2025. hal-05085596

HAL Id: hal-05085596

https://hal.science/hal-05085596v1

Submitted on 26 May 2025

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Distributed under a Creative Commons Attribution 4.0 International License
## INTRODUCTION

Motor imagery-based neurofeedback (MI-NF) relies on the extraction of reliable and physiologically meaningful sensorimotor brain activity from electroencephalography (EEG) signals [1]. Traditional spatial filtering approaches, such as Laplacian filter centered over sensorimotor regions (e.g., C3 and C4), are widely used due to their simplicity and their alignment with well-established neurophysiological knowledge. However, this approach does not take **inter-individual variability** into account [2].

In contrast, **Common Spatial Patterns (CSPs)** provide a subject-specific method; they maximize variance differences between conditions to enhance brain signal discriminability [3]. However, they are sensitive to noise, such as non-neural artifacts, and can fail to consistently reflect physiologically plausible spatial patterns [4]. These limitations raise concerns regarding their robustness and suitability for neurofeedback applications, where interpretability and specificity are essential.

→ **Aim:** Evaluate **CSP reliability** in the context of **neurofeedback** applications.

> **Consistency of CSP-derived spatial filters** across individuals.

> **Correspondence with established spatial patterns** related to MI-NF.

## METHODS

<figure><img src="image_2.png" /></figure>

### Participants & Experimental Design

- EEG: 32 electrodes (actiCHamp plus; SR = 1kHz; DC-280Hz; reference electrode: Fz).

- 20 subjects (12 females, age: 28.5 ± 6.7 years [mean ± SD])

- A single MI-NF session of right-hand motor imagery.

<figure><img src="image_3.png" /><figcaption>Virtual hand (HAND)</figcaption></figure>

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

### CSPs Calibration (8-30 Hz)

**2 conditions**

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

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

Raw Data

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

ICA-corrected
data

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

### CSPs Selection

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

#### Form-based selection by 2 experts

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

<figure><img src="image_10.png" /><figcaption>Patterns</figcaption></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_11.png" /></figure>

#### Automatic selection via Representational Similarity Analysis (RSA) [5]

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

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

#### Standard CSP Selection

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

Maximize Contrast
between Conditions

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

Minimize Variance during
MI

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

### Analysis

#### Choice

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

Cohen's kappa

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

Grand average of
selected CSPs

#### Confidence

Mean and standard
deviation

#### NF Performance

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

## RESULTS

### A. Mean and Standard Deviation of Selected CSPs Across Experts and RSA, With/Without ICA Correction

<figure><img src="image_19.png" /><figcaption>Expert 1</figcaption></figure>

<figure><img src="image_20.png" /><figcaption>Expert 2</figcaption></figure>

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

<figure><img src="image_22.png" /><figcaption>RSA score: 0.43 (± 0.1)</figcaption></figure>

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

<figure><img src="image_24.png" /><figcaption>Confidence: 3.8 (± 0.87)</figcaption></figure>

<figure><img src="image_25.png" /><figcaption>Confidence: 2.95 (± 1.47)</figcaption></figure>

<figure><img src="image_26.png" /><figcaption>Confidence: 3.55 (± 0.92)</figcaption></figure>

<caption>Confidence: 2.9 (± 1.4)</caption>

### B. Agreement (Cohen's Kappa) Between Experts and RSA, With and Without ICA

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

<figure><img src="image_28.png" /></figure>

### C. Mean of Standard CSP Selection, With/Without ICA Correction

<figure><img src="image_29.png" /><figcaption>Maximize Contrast</figcaption></figure>

<figure><img src="image_30.png" /><figcaption>Min Variance MI</figcaption></figure>

<figure><img src="image_31.png" /></figure>

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

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

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

### D. Neurofeedback Performance: Laplacian vs Expert- and RSA-Based CSPs

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

## DISCUSSION / CONCLUSION

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

### Key Insights & Limitations

- ICA correction improved the consistency of CSP selection, increasing agreement between experts and with the RSA-based method, with no effect on confidence levels.

- Expert-selected CSPs were generally more physiologically plausible. However, inter-expert variability remains present.

- The RSA was able to detect plausible physiological activity when it was present but exhibited substantial variability across subjects.

- No NF performance improvement was observed compared to a classical Laplacian.

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

### Perspectives

- Restrict analysis to motor cortex electrodes [4] and explore alternative similarity metrics, such as optimal transport. [6]

- Increase the number of expert raters.

## REFERENCES

[1] Dussard C., Pillette L., Dumas C., Pierrieau E., Hugueville L., Lau B., Jeunet-Kelway C., George N. Influence of feedback transparency on motor imagery neurofeedback performance: The contribution of agency. J. Neural. Eng. 2024 ; 21(5). doi: 10.1088/1741-2552/ad7f88

[2] Lu J., McFarland D. J., Wolpaw J. R. Adaptive Laplacian Filtering for Sensorimotor Rhythm-Based Brain Computer Interfaces. J. Neural. Eng. 2013 ; 10(1): 016002. doi:10.1088/1741-2560/10/1/016002

[3] Blankertz B., Tomioka R., Lemm S., Kawanabe M., Muller K. Optimizing Spatial Filters for Robust EEG Single-Trial Analysis. IEEE Signal Processing Magazine 2008 ; 25(1), 41-56. doi:10.1109/MSP.2008.4408441

[4] Lioi G., Butet S., Fleury M., Bannier E., Lécuyer A., Bonan I., Barillot C. A Multi-Target Motor Imagery Training Using Bimodal EEG-fMRI Neurofeedback: A Pilot Study in Chronic Stroke Patients. Front. Hum. Neurosci. 2020 ; 14-37. doi: 10.3389/fnhum.2020.00037

[5] Kriegeskorte N., Mur M., Bandettini P. Representational Similarity Analysis – connecting the branches of systems neuroscience. Front. Syst. Neurosci. 2008 ; 24, 2:4. doi:10.3389/neuro.06.004.2008

[6] Chu C., Zhu L., Huang A., Xu P., Ying N., Zhang J. Transfer learning with data alignment and optimal transport for EEG based motor imagery classification. J. Neural. Eng. 2024 ; 21, 016015. doi:10.1088/1741-2552/ad1f7a

**Acknowledgements:** This work was supported by the French National Research Agency (BETAPARK project, ANR-20-CE37-0012).

## ABSTRACT

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