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# Validating neurophysiological predictors of BCI performance on a large open source dataset

David Trocellier, Bernard N'Kaoua, Fabien Lotte

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David Trocellier, Bernard N'Kaoua, Fabien Lotte. Validating neurophysiological predictors of BCI performance on a large open source dataset. GBCIC 2024 - 9th Graz Brain-Computer Interface Conference 2024, Sep 2024, Graz, Austria. hal-04696176

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VALIDATING NEUROPHYSIOLOGICAL PREDICTORS OF BCI
PERFORMANCE ON A LARGE OPEN SOURCE DATASET

D.Trocellier<fnref n="1" />,<fnref n="2" />, B. N'Kaoua<fnref n="3" />, F. Lotte<fnref n="1" />,<fnref n="2" />

E-mail: david.trocellier@inria.fr

ABSTRACT

Brain-computer interfaces (BCI) are systems that process brain activity to decode specific commands from it such as motor imagery patterns generated when users imagine movements. Despite the growing interest in BCI, they present significant challenges, notably in decoding distinct neural patterns, due to considerable variability across and within users. The literature showed that various predictors were correlated with subject's BCI performance. Among these indicators, neurophysiological predictors appeared to be the most effective, although studies generally involved small samples and results were not always replicated, thus questioning their reliability. In our study, we used a large dataset with 85 subjects to analyse the relationship between different predictors identified in the literature and BCI performance. Our findings reveal that only four of the six predictors tested could be replicated on this dataset. These results underscore the necessity of validating literature findings to ensure the reliability and applicability of such predictors.

INTRODUCTION

Brain-computer interfaces (BCI) are devices that measure brain activity, typically through electroencephalography (EEG), to extract specific commands for computer input. A particularly widespread paradigm of BCI is motor imagery (MI), which involves decoding EEG patterns characteristic of movement imagination (typically signals from 8-13 Hz located over the motor and sensory motor cortices). MI leads to patterns similar to motor execution, characterized by an event-related desynchronization (ERD), i.e., a diminution of the amplitude, within the mu band (8-13 Hz) and beta band (13-30 Hz) of the contralateral sensory motor cortex, followed by an event-related synchronization (ERS), i.e. an augmentation of the amplitude of the signal within the beta band after the imagined movement ends. A primary limitation of current MI-BCI technology is decoding accuracy. Producing clear neurophysiological signals that can be decoded by existing classification algorithms is not a competence that all BCI users have. In their article, Blankertz et al. [1] demonstrate that using a common spatial pattern (CSP) with a

linear discriminant analysis (LDA) classifier, the average BCI accuracy is 74.4 % ± 16.5 %, with significant variability among participants, ranging from perfect classification (100 %) to performance equivalent to chance level (50 %). More recently, Dreyer et al. [2] published a large database of 87 first-time BCI users performing MI-BCI, reporting a mean accuracy of 63.53 % with a large variability of performances (std = 17.61 %). This variability may be due to users' inability to produce clear and distinguishable patterns that are strong enough to be classified by current algorithms. It is considered that for effective BCI control, performance should exceed 70 % [3]: users below this threshold are deemed "BCI illiterate" or the BCI "BCI deficient". Understanding the parameters explaining differences in user control of such devices has been an important research question for the past 20 years. A better comprehension of those predictors is essential for developing better BCIs, e.g., to later identify the best BCI type for each user or to create BCIs that consider those predictors in their design and into classification algorithms. The literature identifies a broad spectrum of predictors that can be categorized into four main groups: personality traits, cognitive profiles, demographic factors, and neurophysiological patterns [4]. Traits are "stable and enduring, caused by internal circumstances" whereas mental states, as defined by Chaplin et al. [5], are "temporary, brief, and caused by external circumstances". Demographic characteristics correspond to personal characteristics (age, gender, etc.), habits, and environment-related factors. Neurophysiological predictors are predictors from the EEG signal during MI tasks, pre-cue MI tasks, or during a resting state, serving as markers of the user's mental states, such as attention [6] [7], fatigue [8], or initial capacities for producing the pattern to be decoded [1].

Among personality traits, cognitive profiles and demographic characteristics correlated with BCI performance. Jeunet et al. [4] identified 3 major elements : user relationship with technology, attentional capacities, and spatial abilities. More recently, Leeuwis et al. [9] re-evaluated these predictors in an experiment with 55 subjects and found that MI-BCI performance was significantly correlated with vividness of visual imagery, and the personality traits of orderliness and autonomy. How-

<footnote n="1">¹ Inria center at the University of Bordeaux, Talence, France</footnote>

<footnote n="2">² LabRI (CNRS - Univ. Bordeaux - Bordeaux INP), Talence, France</footnote>

<footnote n="3">³ Université de Bordeaux, Bordeaux, France</footnote>
ever, Benaroch et al. suggested that personality traits, cognitive profiles and demographic characteristic might not be sufficient to predict MI-BCI performance using statistical models [10]. They conducted a follow-up experiment incorporating into the models neurophysiological predictors measured during a two-minute baseline at rest [11], and measured their predictive capabilities. They found significant predictability with neurophysiological predictors (p<0.01) while traits and demographic information led to a predictability that was not better than chance level (p=0.88).

The most robust neurophysiological predictor of BCI performance is the sensory motor rhythm (SMR) predictor, with a correlation coefficient (R) of 0.53 validated with 80 participants, proposed by [1]. This predictor is computed from a 2-minute EEG recording during an open-eye baseline period, wherein the participant is not engaged in MI tasks associated with BCI activities. The SMR-predictor encapsulates the participant's capability to modulate their SMR. The effectiveness of predictions based on the SMR was confirmed by subsequent studies [11, 12], which also highlights the efficacy of SMR-based predictors. Specifically, Tzdaka et al. [11] introduced additional predictors based on the estimation of the power spectral density (PSD) during the open-eye baseline. With 56 participants, they showed that the mean performance during the session was significantly correlated with the number of frequency peaks necessary to model the PSD at electrodes C3 and C4 (R = 0.351) and with the temporal variance in the amplitude of the peak within the mu band during baseline (R = -0.477). This also highlights the efficacy of baseline SMR-based predictors of MI-BCI performances.

Other predictors have also been correlated with BCI performance. Grosse-Wentrup et al. identified in a dataset of 10 participants a correlation between the high gamma (55-85 Hz) rhythm in centro-parietal and frontal regions during the MI-BCI task and the SMR quality score, a metric of BCI classification performance (r = 0.0786, p = 9.998 × 10⁻⁵) [13]. Here, the gamma rhythm was thought to be related to attentional networks active during MI-BCI tasks [7]. Foong et al. demonstrated that for stroke patients (n=11), the mean relative beta power (12-30 Hz) before the cue across all trials was correlated with session performance [8]. This correlation was present for the relative beta power in the frontal (F3,Fz,F4)(r = 0.251, p = 0.0005) and central (C3,Cz,C4)(r = 0.181, p = 0.0130) brain regions but not in parietal-occipital regions (r = 0.033, p = 0.6486). It is important to note that this study made the hypothesis (not empirically verified) that relative beta power was a neural correlate of fatigue. Ahn et al. [14] divided the users in two different groups based on their performances during MI-BCI training, and have shown that during the baseline the efficient group has significantly higher θ and lower α than the inefficient one. Based on these results, they proposed a predictor of BCI performance, named PPfactor, that combined θ, α, β and γ power which had a strong correlation with BCI

performance (R = 0.59) in their 61 subject dataset. Interestingly, although no significant correlations were found between BCI classification performance and either β or γ power, they were nonetheless included in the PPfactor equation.

Despite the extensive literature in the field, identifying strong and robust predictors of MI-BCI remains a significant hurdle. While the SMR predictor efficacy has been replicated in other datasets [11, 12], it is not the case for the other predictors. Botrel et al. have highlighted the issue, where numerous studies introduce new potential psychological or neurophysiological predictors of performance, yet often fail to replicate the findings of earlier research [15]. Moreover, several of these predictors were identified on small data sets, which can question their reliability. There is thus a pressing need to consolidate these predictors within a single study, on a different data set than the one on which they were identified, to assess their validity and reliability comprehensively. Therefore, in this paper, we aim to evaluate these diverse neurophysiological predictors together using a large (n=85) open source dataset to attempt to confirm their correlations with BCI performance.

This paper is organised as follows. In the Materials and Methods section, we present the dataset used and the methodology for extracting the neurophysiological predictors. The Results Section analyses the correlation between BCI performance and the neurophysiological predictors extracted. The Discussion Section finally compares those results with the one obtained by the original articles.

## MATERIALS AND METHODS

In order to validate those different neurophysiological predictors on a large and open source data set, we need to first extract those predictors from the dataset and then correlate them with BCI performance. In this section, we will first briefly present the dataset, then we will detail the 6 different neurophysiological predictors and how to compute them, lastly we will develop the statistical analysis that we performed.

*Dataset:* The dataset used in this analysis is sourced from an open-access EEG database with 87 participants, collected during a single day of MI-BCI experiments [2]. The experimental protocol was organized into a single session of motor imagery, divided into six runs. Before the first runs the participants were asked to perform two three minutes baseline recordings, one with open eyes, the other with closed eyes, where the participants were asked to fix a cross and relax. Participants were then required to engage in 20 motor imagery tasks (trials) for each hand per run. The first two runs corresponded to a calibration phase, where the feedback provided does not reflect the participants' actual motor imagery performance, but was a sham feedback. Based on the EEG data from these two first runs, a linear classifier employing three pairs of CSP spatial filters and a LDA classifier is
trained. In the subsequent four runs, participants received real-time feedback, visually represented by a horizontal bar whose length varied in proportion to the accuracy of the classifier predictions. The goal for participants was to maximize the length of this bar through their motor imagery efforts. In this analysis, we used epoch-wise accuracy (EAcc) as classification performance metrics. This metrics is expressed in ratio of correctly classified epochs compared to the total number of classification. We divided each MI trial in 1 seconds epoch with 1/16 overlap from 0.5 to 4.5 seconds after the visual cue.

Two participants, identified as A40 and A59, were excluded from the statistical analysis due to missing trials that could potentially impact the data analysis pipeline in subsequent steps.

### Neurophysiological predictors:

In this paper we focused on six distinct predictors, four of them are extracted during the two-minute eyes-open (OE) baseline, one during the motor imagery (MI) trial, and one across MI trials. The extraction of these neurophysiological predictors was conducted as delineated in their respective foundational studies. The following subsections detail the extraction process for each predictor.

**The SMR-predictor** [1] is computed from a 2-minute EEG recording, taken during an open-eye baseline period when the user is relaxing. The signal is filtered between 4 and 40 Hz using a Laplacian filter around the C3 and C4 electrodes. The SMR-predictor is computed based on an estimation of the power spectral density (PSD) composed by the sum of two functions, as shown in Equation 1. The function $g_1$, given in Equation 2, models the noise floor of the signal, while $g_2$, outlined in Equation 3, estimates the peaks in the $\mu$ (8-15 Hz) (with $\mu_1$ and $\sigma_1$) and $\beta$ (15-30 Hz) (with $\mu_2$ and $\sigma_2$) bands in order to best estimate the PSD during the baseline. The SMR predictor is defined as the maximum difference within the mu band between the noise floor and the estimated peak $g_2$. From the PSD estimation, Tzdaka et al. [11] found two other predictors correlated with BCI performance: **The sum of the number of peaks** in C3 and C4 for each PSD reconstruction $g$ (either 0, 1, or 2 for each channel) and **the variance across time of the peak frequency of the SMR predictor during the baseline**. For the latter, the two-minute baseline signal is divided into 10-second epochs with a 3-second overlap. For each epoch, the PSD of the signal is estimated with $g$. The variance is computed across all the estimations of the SMR predictor for C3 and C4, and then averaged between the two sensors.

$$g(f; \lambda, \mu, \sigma, k) = g_1(f; \lambda, k) + g_2(f; \mu, \sigma, k) \quad (1)$$

$$g_1(f; \lambda, k) = k_1 + \frac{k_2}{f^{\lambda}} \quad (2)$$

$$g_2(f; \mu, \sigma, k) = k_3\phi(f; \mu_1, \sigma_1) + k_4\phi(f; \mu_2, \sigma_2) \quad (3)$$

where $k = (k1;k2;k3;k4) \in \mathbb{R}^4$; $\lambda \in \mathbb{R}$ correspond to the steepness of the noise floor and $\phi(., \mu, \sigma)$ indicates the

probability density function of a normal distribution with mean $\mu$ and standard deviation $\sigma$.

**The PPfactor** is calculated using data from a two-minute baseline period with open eyes [14]. During this phase, after applying a common average reference and a notch filter centred at 50 Hz to mitigate power line interference, the power spectrum of the EEG signal at electrodes C3 and C4 is analysed across various frequency bands: $\theta$ (4-8 Hz) $\alpha$ (8-13 Hz), $\beta$ (13-30 Hz) and $\gamma$ (30-70 Hz). The power values for each band are then normalized by the total power across the entire spectrum (4-70 Hz) to account for individual differences in overall brain activity levels. The PPfactor is subsequently calculated using Equation 4

$$PPfactor = \frac{\alpha + \beta}{\theta + \gamma} \quad (4)$$

**The high gamma predictor** is a marker of attention, extracted during the MI task [7]. During the entire epoch of the MI task, only the electrodes from the Frontal, Central, Central Parietal, and Occipital regions are considered. The EEG signal from these regions is filtered between 55 and 85 Hz to isolate the gamma band. The signal is then processed as follows: The filtered EEG signal is squared to calculate the power of the signal at each time point within the epoch. The mean power is computed by averaging the squared signal over all time stamps within the epoch. The power values are then log-transformed to normalize the distribution. Finally, the mean across all selected channels is computed to obtain a single value representing the mean log power for the gamma band during the MI task. It is important to note that the signal used for classification of MI is filtered between 8 and 30 Hz. Therefore the gamma band is not used as a classification feature.

**The Relative beta power at rest** is derived from a 3-second pre-MI task EEG segment, without explicit rest instructions, using frontal electrodes (F3, Fz, F4) [8]. As presented in equation 5 it is computed from the power $\omega$ in the broad band (4-50 Hz) and the power $\beta$ in the beta band (12-30 Hz) and is thought to reflect the level of fatigue of the user during the task.

$$RelativeBeta = 10 * \log_{10}\left(\frac{\beta}{\omega}\right) \quad (5)$$

**Statistical analysis:** We conducted correlations between the predictors and the offline classification performances. Our approach varied depending if the predictors is compared with the overall performance during the session or if this predictor is compared with the evolution of performance during the session. In the first case we performed Spearman correlation and in the second multi repeated correlation analysis [16]. The detailed analysis is described below:

- For predictors obtained during the baseline phase, we explored the correlation between each subject's overall performance in a single session and the predictor. We used Spearman correlation tests for this analysis. Spearman correlation was chosen for its ability to capture
Analysis of correlation between neurophysiological factors extracted during the baseline and subject's performance

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

Figure 1: Correlation between subjects' BCI performance and neurophysiological predictors extracted during baseline. The scatter plots from left to right represent: SMR-predictor, the number of PSD peaks in the sensorimotor channels C3 and C4, the variance in the peak of μ-band frequency during baseline, and the PPfactor. Each plot displays individual subject performances against the respective neurophysiological predictor, with the fitted linear regression line illustrating the trend. Significance levels (p-values) and correlation coefficients (r) between each predictor's and the performance are indicated based on Spearman correlation.

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

Figure 2: repeated measure correlation analysis between individual subjects' BCI performances and their average relative beta power during rest phases across four runs (n=85 subjects). The lines correspond to the regression for each subject where the slope is fixed and correspond to the repeated measure correlation coefficient (r = 0,0043).

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

Figure 3: repeated measure correlation analysis between SMR quality score and the high gamma predictor power during the 40 trials of MI (n=85 subjects). The lines correspond to the regression for each subject where the slope is fixed and correspond to the repeated measure correlation coefficient (r = 0,1056).

monotonic relationships without assuming linearity, for possibly being more robust to outliers and for reflecting better neurophysiological dynamics [17].

- For relative beta power at rest, we assessed the multi repeated correlation for each subject [16] between performance during the run and the mean relative beta power during resting phases before the MI trials. Repeated measures correlation is a statistical technique for determining the common within-individual association for paired measures assessed for multiple individuals, in this case the mean performance and the relative beta power at rest. Note that the original paper [8] found correlations between beta power at rest and BCI performance across sessions while we assess it across trials for a single session.

- For high gamma predictor, we investigated the correlation between the SMR quality score and high gamma predictor during MI. The SMR quality score, as defined in the work of Grosse-Wentrup and colleagues [7], is the

output of the LDA classifier (negative for left-hand MI and positive for right-hand MI) multiplied by the sign of the actual class. Consequently, correct trials yield a positive SMR quality score, whereas incorrect ones result in a negative score. It is essential to recognize that the magnitude of the LDA output (positive or negative) directly relates to the model's confidence in its classification, indicating that higher positive SMR quality scores denote accurate predictions made with high confidence, and higher negative scores denote incorrect predictions made with similar confidence. Correlations were conducted with repeated measures correlation per subject to appropriately analyse these relationships.

## RESULTS

In the analysis of neurophysiological predictors extracted during the baseline phase, we investigated the correlation between each predictor and the subjects' performance during the entire session. Figure 1 illustrates the Spear-
man correlation coefficients with the linear regression be-
ing also added to illustrate the correlation. The most
significant predictor of performance was found to be the
SMR predictor, which exhibited a positive and significant
Spearman correlation (ρ = 0.49, *p* < 0.01). The second
most significant predictor was the variance of the peak
frequency necessary to reconstruct the PSD, as defined in
Equation 3. This predictor showed a significant negative
Spearman correlation (ρ = −0.33, *p* = 0.0029). The third
predictor evaluated was the number of peaks required to
model the PSD during the baseline, demonstrating signif-
icant Spearman correlation (ρ = 0.24, *p* = 0.028). The
PPfactor did not exhibit any significant correlation with
performance (ρ = 0.07, *p* = 0.50).

The analyse of the correlation between the mean rel-
ative beta power at rest and the corresponding perfor-
mance, depicted in Figure 2, reveals no significant rela-
tionship (r = 0.0043, p = 0.94). In contrast, the correla-
tion between gamma predictor and SMR quality scores,
as shown in Figure 3, is weak but very statistically signif-
icant correlation (r = 0.1056, p = 9 × 10⁻¹⁵).

DISCUSSION

The identification of robust and reliable neurophysio-
logical predictors of BCI performance is a challenging
and promising research issue. In this study, we success-
fully validated only four out of six proposed neurophys-
iological predictors using a large dataset comprising 85
subjects. Among these validated predictors, three were
extracted during the baseline and demonstrated correla-
tions with the mean session performance. Interestingly,
the high-gamma predictor showed a correlation with the
SMR quality score, which allows a trial-wise analysis to
understand performance variations. In contrast, the pre-
dictive values of PPfactor and relative resting beta were
not replicated in this dataset.Two important differences
between our study and the initial study showing a corre-
lation with relative beta [8] could explain these discrep-
ancies. Firstly, that predictor was identified in stroke pa-
tients, a group often affected by fatigue [18]. Since rela-
tive beta power was used as a neurophysiological indica-
tor of fatigue, it may have greater predictive value in this
patient population rather than in the general population.
Secondly, in their research, the repeated measure corre-
lation was conducted across multiple sessions, while in
our study, it was assessed across runs within a single ses-
sion with only four runs per subject. Therefore, relative
beta power may serve as a better indicator of overall fa-
tigue rather than reflecting the evolution of fatigue within
a session. Further analysis of this predictors will be nec-
essary to confirm this relationship. Regarding the PPfac-
tor, its lack of reproducibility cannot be attributed to dif-
ferences in the experimental setup. Previous studies at-
tempting to replicate this predictor only found significant
correlations in one out of two datasets they tested [19].
Notably, when they applied a Laplacian filter around the
C3 and C4 electrodes before extracting the PPfactor, this

enhanced the correlation between this predictor and per-
formance metrics as compared to using and C3 and C4
without Laplacian filters. Therefore, incorporating this
filtering step into the PPfactor computation may be es-
sential for enhancing the predictor's significance.
While previous studies have suggested that demograph-
ical information and personality traits [10, 15] may not
be reliable predictors of perfomance, our study show the
reliability and the reproducibility of most neurophysio-
logical predictors.

CONCLUSION

In this paper, we compiled various studies that identify
neurophysiological predictors of MI-BCI performance
across and within different subjects. We noted that these
predictors have predominantly been explored in small
datasets and lacked widespread replication on indepen-
dent data sets. Therefore, we sought to replicate the
predictive value of six distinct indicators within a large
(n=85), open source dataset. Of these, we managed to
replicate the results of 4 of them: the SMR predictor, the
number of peaks necessary for estimating the PSD during
the baseline, the variability across time in the peak fre-
quency within the mu band during baseline, and the high
gamma predictor during the trial. However, the PPfactor
and the relative beta power at rest were not successfully
replicated. This study reinforces the significance of the
SMR predictor as a robust indicator of BCI performance
and highlights the critical need for replication of results
reported in the BCI literature.

Future research needs to explore the influence of classifi-
cation algorithm on predictors of BCI classification per-
formance and validate that the predictors that we validate
in this article are reliable predictors independently of the
classifier used.

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