# From Post-Median Nerve Stimulation ERD to MI-BCI Expertise Prediction

Valérie Marissens Cueva, Fabien Lotte, Laurent Bougrain, Sébastien Rimbert

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Valérie Marissens Cueva, Fabien Lotte, Laurent Bougrain, Sébastien Rimbert. From Post-Median Nerve Stimulation ERD to MI-BCI Expertise Prediction. BCI 2025 - 13th International Winter Conference on Brain-Computer Interface, Feb 2025, Seoul, South Korea. pp.1-6, 10.1109/BCI65088.2025.10931645. hal-04981146

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# From Post-Median Nerve Stimulation ERD to MI-BCI Expertise Prediction

Valérie Marissens Cueva<fnref n="1" />,², Fabien Lotte<fnref n="1" />, Laurent Bougrain²,³ and Sébastien Rimbert<fnref n="1" />

***Abstract***—Predicting performance in Motor Imagery-based Brain-Computer Interfaces (MI-BCIs) is essential for identifying potential users and improving system efficiency. This study investigates whether Event-Related Desynchronization (ERD) induced by Median Nerve Stimulation (MNS) can predict a user's MI-BCI performance group (low, medium, or high) before system use. We analyzed neurophysiological features from post-MNS ERD using Linear Discriminant Analysis (LDA) and Least Absolute Shrinkage and Selection Operator (LASSO). Both achieved 74% accuracy for two-group classification (low vs. high performers), while hierarchical clustering demonstrated 83% accuracy for unsupervised classification, emphasizing MNS's discriminative power. For three-group classification, LDA performed best with 58% accuracy. Topographical analyses further revealed stronger ERD responses in motor-related areas for higher performers during both MNS and MI tasks, highlighting MNS-induced ERD as a reliable, practical and non-invasive predictor of MI-BCI performance levels for early user stratification and advanced personalized BCI.

***Index Terms***—Brain-Computer Interface; Median Nerve Stimulation; Motor Imagery; Performances; Electrophysiology; Event-Related Desynchronization; Predictors; Users Profiling.

## I. INTRODUCTION

Motor imagery (MI) is a widely explored paradigm in Brain-Computer Interfaces (BCIs), enabling users to interact with systems through mental movement representations [1]. This approach has significant potential in rehabilitation, assistive technologies, and communication for individuals with motor impairments [1]. However, approximately 30% of participants fail to achieve sufficient MI-BCI control [2], and even successful users often fall short of sufficient practical classification thresholds [3], a phenomenon termed BCI illiteracy. Factors such as individual differences in motor imagery ability, variations in cortical activity, psychological states, and external noise create challenges for MI-BCI implementation and limit its broader adoption in clinical and real-world settings [4].

Given these challenges, accurately predicting a user's ability to use a MI-BCI is crucial for improving user experience, optimizing experimental designs, and enhancing clinical applications. Indeed, predictive tools can help researchers tailor BCI parameters to individual users, maintaining engagement and maximizing performance [4]. Predictive tools could also improve system calibration, guide user training, and help

match individuals to the BCI paradigms most suited to their abilities. For BCI research in general, subject selection based on predicted performance can reduce variability, leading to more reliable results and deeper insights into performance-related neurophysiological factors [5], [6]. In the rehabilitation area, such predictions are especially valuable for personalizing MI-BCI protocols, ensuring resources are directed toward patients most likely to benefit, and ultimately improving therapeutic outcomes [7], [8].

Predicting a user's future performance in MI-BCI remains an open research question [9], explored through neurophysiological signals and/or questionnaire-based assessments. While various approaches have been investigated to identify reliable predictors, consistent findings remain elusive. Neurophysiological methods show moderate correlations with performance, such as mu peak power during baseline (r = 0.53; [10]), spectral entropy (r = 0.65; [11]), and the meanSP predictor analyzing nonlinear interactions between mu and beta rhythms (r = 0.53; [12]). Other metrics, including sensorimotor connectivity (r = 0.41; [13]), EEG spectral slopes, or variability in mu peak features (r = 0.4–0.6; [14]), provide insights but often lack cross-dataset generalizability. Subjective and psychometric questionnaires, such as the Motor Imagery Questionnaire (MIQ; [8]), Kinesthetic and Visual Imagery Questionnaire (KVIQ; [15]), Sixteen Personality Factor questionnaire (16PF5; [16], [17]), and Mental Rotation Test [18], have also been explored but often yield inconsistent results, highlighting the complexity and variability of individual MI-BCI performance.

Median Nerve Stimulation (MNS), a painless low-amplitude electrical stimulation applied to the wrist, has recently emerged as a promising method for predicting MI-BCI performance [6], [19]. MNS induces rapid and robust modulation of sensorimotor and motor cortical activity, observable in EEG as event-related desynchronization (ERD) [20], [21], which is faster but similar in shape to movement intention [6], [20]. Our preliminary studies report a strong correlation (rho = -0.71) between MNS-evoked ERD features and MI-BCI performance, highlighting its potential as a reliable predictor [6], [19]. Beyond this correlation, MNS offers three key advantages: it produces ERD/ERS patterns indicative of motor imagery readiness, is passive and features a relatively simple setup that integrates seamlessly into existing BCI protocols. With only 2 minutes of MNS (20 stimulations), the correlation with MI-BCI accuracies is still significant (more details in [6], [19]).

Although many studies have explored correlations between neurophysiological predictors and MI-BCI performance, few

<footnote n="1">¹S. Rimbert, V. Marissens Cueva and F. Lotte are with the Inria Center at the University of Bordeaux / LaBRI (CNRS / Univ. Bordeaux / Bordeaux INP), 33405, Talence, France. ²L. Bougrain and V. Marissens Cueva are with the Université de Lorraine, CNRS, LORIA, F-54000 Nancy, France. ³L. Bougrain is with Sorbonne Université, Institut du Cerveau – Paris Brain Institute -ICM, CNRS, Inria, Inserm, AP-HP, Hopital de la Pitié Salpêtrière, 75013 Paris, France. sebastien.rimbert@inria.fr</footnote>
have advanced to building predictive models capable of esti-
mating users' future BCI control performance [14], [19]. A
strong correlation does not guarantee accurate predictions, as
challenges like signal variability, overfitting in small datasets,
and model generalizability persist. In prior work [19], the
authors showed that post-MNS features achieved a predictive
correlation of r = 0.61 between predicted and actual accuracies
of unseen subjects, demonstrating their potential for MI-BCI
performance prediction. However, precise individual perfor-
mance prediction remains complex and often impractical. In-
stead, classifying users into broader performance groups, such
as high performers versus those near 'BCI-illiteracy' (<70%
accuracy), may offer a feasible alternative. This study explores
whether MNS-induced ERD features can reliably predict ex-
pertise groups using classification methods, providing a novel
approach to leveraging neurophysiological signals for user
categorization. Such stratification could enhance BCI system
design, allowing tailored adaptations for specific user groups
and improving both system efficiency and user experience.

II. MATERIAL AND METHODS

A. Participants

31 healthy subjects (8 females; aged 27.38 years old; STD = 11.48) participated in this study. The study followed the ethical principles of the WMA Declaration of Helsinki on medical research involving human subjects [22]. Informed consent was obtained from all participants, with approval from Inria's local ethics committee (COERLE, approval number: 2016-011/01).

B. Motor tasks

This study involved two conditions: kinesthetic motor im-
agy and passive median nerve stimulation. As the experiment
was initially designed to investigate the impact of general
anesthetic conditions on EEG (see [23], [24] for more details),
participants were placed lying on a comfortable bed with their
eyes closed. Their right forearm was placed on a cushion to
minimize movement. The order of the two conditions was
randomized across subjects.

1) *Right-hand motor imagery:* For the MI condition, sub-
jects were instructed to imagine a discrete pinch movement
between the thumb and index finger, in order to evoke as
many sensations as possible typically resulting from actual
movement, without physically moving [25]. A low-frequency
beep triggers the start of the MI task, which was held for 2
seconds, after which a second beep triggers the end of the
imagined task. Before the study, subjects underwent progres-
sive training to perform the MI task (see [26] for more details).

2) *Right-hand median nerve stimulation only:* The MNS-
only condition involved a series of passive MNS during a
resting state. Two stimulation electrodes were placed on the
wrist according to established standards [27], [28], ensuring
that the stimulation produced a slight movement between the
thumb and index finger. Stimulation intensity was individually
adjusted for each subject, ranging from 8 to 15 mA.

C. Electrophysiological recordings

EEG signals were recorded using the OpenViBE software
platform and a Biosemi Active Two 128-channel EEG system,
with a sampling rate of 2048 Hz. The EEG was recorded
from 128 sites following the ABC layout. An external elec-
tromyogram (EMG) electrode was used under all conditions
to confirm that no movement occurred during the MI task.
Impedance was maintained below 10 kΩ for all electrodes to
minimize background noise in the recorded signal.

D. Topographic analysis

As a reminder, previous studies have shown that MNS elicits
distinct neurophysiological patterns (see Figure 1), including
three main phases: an initial Post-Stimulation Rebound (PSR),
followed by an ERD phase (200-800 ms) observed in both mu
(7-13 Hz) and beta (15-30 Hz) bands, and a second, broader
ERS phase mainly affecting the beta band after 800 ms [6],
[20]. Topographic analyses indicate that the MNS-induced
ERD is more bilateral than the predominantly contralateral
ERD observed during motor imagery, though the spatial and
temporal patterns between the two conditions remain similar
overall (see Figure 2). These findings suggest that MNS-
induced ERD provides a robust marker for motor cortex acti-
vation, comparable to the patterns elicited by motor imagery.

To compare the MI and MNS conditions for different MI-
BCI expertise groups, we performed an event-related spectral
perturbation (ERSP) analysis in the 8-30 Hz range. These
topographic analyses were performed using the EEGLAB
toolbox [29] and MATLAB R2023a (The MathWorks, Inc.).

E. ERD/ERS modulations

To predict BCI performance from MNS-induced motor
patterns, we quantified the ERD and ERS using the band
power method [30] with a Matlab script. EEG signals were
first filtered in the mu (7-13 Hz), mu-beta (8-30 Hz) or beta
(15-30 Hz) bands using a 4th-order Butterworth filter. The
signals of each trial were squared and then averaged across
trials and smoothed with a 1 s sliding window and a 100 ms
shift. The sliding window was chosen due to the nature of the
MI task and the ERD/ERS components. Lastly, we subtracted
the baseline power (from -3 to -0.5 seconds) from the average
power of each window, then divided by the baseline power,
and finally multiplied by 100 to obtain percentages.

F. Classification of MI-BCI expertise

Our predictor for MI-BCI expertise is based on the mini-
mum value during the ERD, representing the strongest desyn-
chronization, within the 200-800 ms window following MNS.
This time window was chosen as it corresponds to the post-
MNS ERD peak (see Figures 1, 2). For each subject, we
computed three predictors: the minimal ERD value during
200-800 ms in the mu (7-13 Hz), mu+beta (8-30 Hz), and
beta (15-30 Hz) frequency bands, on electrode C3 with a small
Laplacian filter.
Offline BCI performances were computed for a right-hand motor imagery vs. rest task. The EEG preprocessing is extensively detailed in [19]. Classification between MI and resting periods was performed using 40 electrodes placed on motor areas. A Tangent Space Logistic Regression (TSLR) classifier was trained using the first 70% of trials and tested on the remaining 30%. Test set classification accuracy determined the BCI performance. Subjects were then divided into two expertise groups based on the median accuracy, or into three groups using the 1/3 and 2/3 quantiles.

To predict users' BCI performance groups based on MNS features, we applied three classifiers: Linear Discriminant Analysis (LDA) and Logistic Regression with (i) Least Absolute Shrinkage and Selection Operator (LASSO) or (ii) ElasticNet regularization. All classifications were performed using Python 3.10 with the Scikit-learn [31], MNE [32], and pyriemann [33] libraries. We evaluated the LDA, LASSO, and ElasticNet classification models using leave-one-subject-out cross-validation. Each model was trained on all but one subject and tested on the excluded subject, with accuracy averaged across all iterations. For the Logistic Regressions, an inner cross-validation was performed to determine the optimal value of C, the inverse of regularization strength, among {0.0001, 0.001, 0.01, 0.1, 1.0, 10, 100, 1000, 10000, 100000} using 10 folds. The best C was selected based on the smallest mean absolute error (MAE) between actual and predicted BCI expertise groups in the inner cross-validation. For three-class classification, the LDA algorithm used a one-vs-one strategy, training classifiers for each pair of classes.

An unsupervised Hierarchical Clustering (HC) algorithm [34] was also used to group subjects based on BCI performance, using only MNS features. Clustering was performed using Euclidean distance and a linkage criterion that minimized the within-cluster sum of squared differences. The resulting dendrogram (Figure 5) visualizes the clusters formed from each subject's MNS features.

<figure><img src="image_2.png" /><figcaption>Fig. 1: Event-related spectrum perturbation of Median Nerve Stimulation at electrode C3, averaged among 31 subjects, figure from [19]. PSR: post-stimulation rebound; ERD: event-related desynchronization; PMBR: post-movement beta rebound.</figcaption></figure>

## G. Statistical analysis

To evaluate differences in post-MNS ERD across BCI expertise groups, we conducted ANOVA and t-tests (see Figure 2). ANOVA assessed overall ERD differences for two-group (low vs. high performers) and three-group (low, medium, and high performers) comparisons. Post-hoc pairwise unpaired two-tailed t-tests examined specific differences between the three groups, i.e., low vs. medium, low vs. high, and medium vs. high. All tests were performed on normalized ERD values within the 200-800 ms post-MNS time window.

To assess whether the classification results were statistically significant compared to chance levels (see Table I), we applied the inverse binomial law, as outlined in [35]. This method considers the number of trials, classes, and the significance level to determine the accuracy thresholds needed to exceed chance levels with statistical significance, ensuring the robustness of our findings.

<figure><img src="image_3.png" /><figcaption>Fig. 2: Scalp topographies of EEG power (8-30 Hz) for right-hand Motor Imagery (MI) and Median Nerve Stimulation (MNS) conditions, 200-800 ms after the go signal. Left: average across three balanced groups for low, medium, and high performers; right: average across two balanced groups for low and high performers.</figcaption></figure>

# III. RESULTS

## A. Influence of MI-BCI expertise on ERD modulations

When participants were grouped based on their MI-BCI performance (either into two or three categories), clear EEG motor patterns emerged. Higher-performing participants consistently exhibited stronger ERD during the MI task compared to their lower-performing counterparts (see Figure 2). These differences are statistically significant, as confirmed by an ANOVA for both the two-group comparison (low vs. high performers; *p*<0.001) and the three-group comparison (low, medium, and high performers; *p*<0.001). Pairwise unpaired two-tailed Student's t-tests further demonstrated significant differences in the three-group case for low vs. high performers
<figure><img src="image_4.png" /><figcaption>Fig. 3: Classification results obtained using LASSO and LDA models with the post-MNS ERD feature to group participants by low or high BCI expertise.</figcaption></figure>

<figure><img src="image_5.png" /><figcaption>Fig. 4: Classification results obtained using LASSO and LDA models with the post-MNS ERD feature to group participants by low, medium, or high BCI expertise.</figcaption></figure>

($p<0.001$) and for medium vs. high performers ($p<0.01$), but not for low vs medium performers ($p=0.54$).

This pattern was also evident during the post-MNS phase, where participants in the high-performance groups displayed significantly greater ERD amplitudes following the somnathetic stimulation (see Figure 2). These results highlight that individuals with better MI-BCI performance exhibit more pronounced cortical responsiveness, both during motor imagery and in response to median nerve stimulation.

## B. Classification of subjects by MI-BCI expertise based on post-MNS ERD features

The accuracy of subjects' classification into BCI expertise groups are summarized in Table I. For the two-group classification (low vs. high performers), the highest accuracy - 74.19% - was achieved with both the LDA and LASSO ($p<0.05$). However, they behaved differently: LASSO tended to better identify high-performing users, while LDA appeared more effective at detecting low-performing users (see Figure 3). This may be due to the LASSO maximizing the correlation of its output with class labels, such correlation being driven by the larger number of high-performing subjects. In contrast, ElasticNet demonstrated a lower accuracy of 58.06%.

<table><caption>TABLE I: Test accuracies for classifying BCI expertise using only the post-MNS ERD features, obtained with a Linear Discriminant Analysis (LDA) model and Logistic Regression with a LASSO or ElasticNet regularization.</caption><thead><tr><th></th><th>LDA</th><th>LASSO</th><th>ElasticNet</th></tr></thead><tbody><tr><td>low vs. high performers</td><td><strong>74.19*</strong></td><td><strong>74.19*</strong></td><td>58.06</td></tr><tr><td>low vs. medium vs. high performers</td><td>58.06</td><td>45.16</td><td>45.16</td></tr></tbody></table>

When extending the analysis to three groups (low, medium, and high performers), classification performance naturally decreased across all classifiers. LDA achieved the highest accuracy at 58.06%, followed by LASSO and ElasticNet, both at 45.16%, yet none of these performances are significantly above chance level. LASSO tended to overestimate medium performers, grouping most of them with high performers, whereas LDA tended to underestimate them by misclassifying them mostly as low performers (see Figure 4). Notably, the same subjects were misclassified by both methods: PM13, M19, M12, PM22, and M7 in the two-group classification, and PM23, M12, M19, M2, PM11, M7, and M11 in the three-group classification. These results are consistent with the findings from topographic analyses. Participants with higher MI-BCI performance consistently demonstrated stronger ERD during MI and after MNS, as shown in Figure 2. This suggests that the differences in cortical motor activation observed across performance groups are sufficiently distinct to enable both supervised and unsupervised classification approaches.

## C. Hierarchical clustering

In addition to the supervised approaches, hierarchical clustering (HC) based on post-MNS ERD features was used to compare the clusters identified in an unsupervised way, with the BCI expertise groups. HC achieved an unsupervised classification accuracy of 83% for binary grouping (low vs. high performers) by matching the clusters with groups defined by the median BCI accuracy. These results further highlight the discriminative power of MNS-induced ERD features, suggesting that these EEG patterns naturally cluster subjects based on their MI-BCI control abilities (see Figure 5).

<footnote>*$p < 0.05$</footnote>
<figure><img src="image_6.png" /><figcaption>Fig. 5: Groups of MI-BCI expertise obtained with Hierarchical Clustering using only the post-MNS ERD features. Mis-matching between BCI performance groups and Hierarchical classification are reported in red and green.</figcaption></figure>

# IV. DISCUSSION

Our study demonstrated that higher MI-BCI performers consistently exhibited stronger ERD during both the MI task and the post-MNS phase, as highlighted in the topographic analyses. This trend was reflected in the classification results, where LDA achieved the highest accuracy (74%) for distinguishing between low and high performers, although performance declined with the inclusion of an intermediate group. These findings emphasize the strong relationship between cortical responsiveness and MI-BCI proficiency, while also illustrating the neurophysiological distinctions and challenges in accurately classifying user performance groups.

## A. Implications for MI-BCI domain

Our findings reveal a significant relationship between ERD strength following MNS and MI-BCI performance, pointing to potential predispositions either in motor cortex activation or its detectability. If a passive task like MNS reliably induces ERD and predicts MI-BCI performance, two key possibilities arise:

1) *Natural predisposition to generate ERD:* Some individuals may inherently activate the motor cortex more effectively, potentially due to physiological or structural factors such as pyramidal neuron organization or motor cortical excitability;

2) *Predisposition for EEG detectability of ERD:* Variability may also stem from how effectively the EEG system captures motor cortex activity, influenced by factors like skull thickness or cortical folding.

Further studies, including within-subject analyses and complementary neuroimaging techniques such as fMRI, are needed to refine our understanding of factors influencing MI-BCI performance. One promising application of these findings is the early identification of users who may struggle with MI-BCI, potentially reducing the prevalence of BCI illiteracy. However, the reliability of MNS-induced ERDs as predictors must be validated, particularly given that neural efficiency from repeated MI practice may lead to reduced ERD amplitudes over time [7], [36].

## B. Future works

1) *Hierarchical clustering:* According to our promising results (see Figure 5), we propose exploring hierarchical clustering (HC)-based classification methods as a complementary approach to conventional forecasting techniques (see Table I). Clustering methods have the potential to uncover intrinsic group structures, which could enhance predictive accuracy, especially when dealing with datasets characterized by subtle intergroup variations. By leveraging the insights gained from clustering, we aim to refine classification models and better adapt them to the complexities of neurophysiological data. For instance, to classify a new subject's BCI expertise group, their MNS-related feature could be compared to the mean MNS features of each group identified by the HC algorithm, assigning the subject to the group with the smallest Euclidean distance to the mean. We also plan to leverage deep learning-based algorithms to detect expertise groups [37].

2) *Integrating multidimensional neurophysiological metrics for enhanced MI-BCI performance:* Integrating multiple neurophysiological metrics into a unified predictive model is a promising direction for future research [14]. For example, combining spectral entropy [11], mu peak activity during resting states [10], [12], and ERD amplitude following median nerve stimulation [6] could offer a comprehensive view of individual BCI capabilities. This multidimensional approach acknowledges that different predictors may hold varying relevance depending on the user. For instance, ERD amplitude may strongly correlate with MI-BCI performance for some, while spectral entropy or resting-state mu peak activity might be more indicative for others due to differences in cortical structure or neural dynamics. By harnessing these complementary features, this strategy aims to capture the diverse neural profiles of users, enhancing MI-BCI systems' accuracy, adaptability, and accessibility.

# V. CONCLUSION

This study demonstrates that Event-Related Desynchronization (ERD) patterns induced by Median Nerve Stimulation (MNS) are promising for predicting Motor Imagery-based Brain-Computer Interface (MI-BCI) performance. High MI-BCI performers showed stronger ERD amplitudes in both motor imagery task and post-MNS phase, reflecting enhanced cortical responsiveness. Classification methods like LDA and LASSO effectively distinguished between low and high performers (74%), while hierarchical clustering successfully identified
tified performance groups in an unsupervised manner. These findings suggest that MNS-induced ERD could serve as a reliable, non-invasive biomarker for early MI-BCI user stratification. MI-BCI experiments could begin with a few MNS trials to predict a user's ability to utilize the system. Future work should develop multifactorial predictors combining neurophysiological and behavioral features to enhance performance prediction and advance personalized BCI applications.

## ACKNOWLEDGMENT

This work has been partially supported by the CHU-Brugmann hospital foundation and the ANR project BCI4IA ANR-22-CE19-0016.

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