# Cortical activations induced by electrical versus vibrotactile finger stimulation using EEG

Anaëlle Alouit, Martine Gavaret, Céline Ramdani, Påvel G Lindberg, Lucile Dupin

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Anaëlle Alouit, Martine Gavaret, Céline Ramdani, Påvel G Lindberg, Lucile Dupin. Cortical activations induced by electrical versus vibrotactile finger stimulation using EEG. NeuroImage, 2025, 314, pp.121249. 10.1016/j.neuroimage.2025.121249. hal-05095115

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# Cortical activations induced by electrical versus vibrotactile finger stimulation using EEG

Anaëlle Alouit<sup>a,*</sup> <sup>●</sup>, Martine Gavaret<sup>a,b</sup>, Céline Ramdani<sup>c</sup>, Påvel G. Lindberg<sup>a,●</sup>, Lucile Dupin<sup>d,●</sup>

## ARTICLE INFO

Keywords:

Primary somatosensory cortex (S1)

Electroencephalography (EEG)

Somatosensory evoked potentials (SEPs)

Finger mapping

Somatotopy

Source localization

Connectivity

## ABSTRACT

Somatosensory evoked potentials (SEPs) recorded with electroencephalography offer insights into cortical responses to tactile stimulation, typically elicited through temporally precise electrical stimulation. Although vibrotactile stimulation is more ecologically valid but less common, studies directly comparing EEG responses to both electrical and vibrotactile finger stimulation are limited. This study examines and compares (a) cortical responses, (b) connectivity patterns, and (c) somatotopic accuracy of these stimulation types on the fingers. In two experiments, SEPs were recorded from healthy participants' right-hand finger stimulation, using either electrical (experiment 1, *n* = 22) or vibrotactile (experiment 2, *n* = 22) stimulation. Vibrotactile stimuli were delivered at 10, 50, and 250 Hz, targeting different ranges of tactile mechanoreceptors activations. Electrical stimulation reliability was assessed across two days, showing consistent SEP amplitudes and latencies. Both stimulation types generated three early components (P1, N1, P2), but all vibrotactile components were increasingly delayed compared to electrical stimulation. Vibrotactile stimulation exhibited stronger localized connectivity in early components (P1, N1) in the left hemisphere, while electrical stimulation showed broader connectivity at P2. Electrical stimulation provided a clearer somatotopic organization in the postcentral gyrus than vibrotactile stimulation. These findings suggest distinct processing for electrical versus vibrotactile finger stimulation. These two stimulation methods are not interchangeable in somatosensory studies: the temporal shift in vibrotactile responses reflects selective activation of Pacinian corpuscles, whereas electrical stimulation yields stronger generalized cortical processing. Electrical stimulation may engage a serial processing pathway starting in the primary somatosensory cortex, while vibrotactile stimulation could involve parallel processing in both primary and secondary somatosensory cortices.

## 1. Introduction

Somatosensory evoked potentials (SEPs) are electrical potentials generated by the brain in response to sensory stimuli, commonly used in both clinical settings to assess sensory pathways and in research to investigate the neural mechanisms underlying sensory perception and integration. SEPs provide a non-invasive window into the real-time cortical processing of somatosensory information. Electrical stimulation, particularly of the median nerve at the wrist, is a common method

to elicit SEPs. Consequently, the related neurophysiological components have been extensively described: N20, P30/P35, N60/P60, P100, P120, N140 (Allison et al., 1992, 1991; Aminoff, 2012; Babiloni et al., 2001; Barba et al., 2004; Forss et al., 1994; Hada, 2006; Jung et al., 2008; Kakigi, 1994; Palmer et al., 2016).

In contrast to median nerve stimulation, which is widely used for both clinical and research purposes, finger nerve stimulation remains relatively uncommon. This gap in research is significant because the sensory processing of the fingers is crucial for understanding tactile

<footnote><sup>a</sup> Université Paris Cité, Institute of Psychiatry and Neuroscience of Paris (IPNP), INSERM U1266, Team “Stroke, from prognostic determinants and translational research to personalized interventions”, 75014, Paris, France</footnote>

<footnote><sup>b</sup> GHU-Paris Psychiatrie et Neurosciences, Hôpital Sainte Anne, Service de neurophysiologie clinique, F-75014, Paris, France</footnote>

<footnote><sup>c</sup> Service de Santé des Armées, Institut de Recherche Biomédicale des Armées, Brétigny-sur-Orge, France</footnote>

<footnote><sup>d</sup> Université Paris Cité, INCC UMR 8002, CNRS, F-75006, Paris, France</footnote>

<footnote>* Corresponding author at: Université Paris Cité, Institute of Psychiatry and Neuroscience of Paris (IPNP), INSERM U1266, 102-108 Rue de la Santé, 75014, Paris, France.</footnote>

<footnote>E-mail address: anaëlle.alouit@inserm.fr (A. Alouit).</footnote>
perception and somatotopic organization in the brain, and finger stimulation could provide insights that differ from those observed in median nerve stimulation. It is primarily used to assess somatosensation integration and processing (Alouit et al., 2024; Forschack et al., 2020), and to a lesser extent, to map cortical representation of fingers (Baumgartner et al., 1991, 1993; Houzé et al., 2011; Kwast-Rabben et al., 2002; Tanosaki et al., 2004). These studies demonstrate that SEPs elicited by finger nerve stimulation typically include a P45/P50 component in the 40–70 ms time window and an N100 around 100 ms, reflecting early cortical processing. Later components, such as N140 (120–150 ms), are also observed.

Nonetheless, electrical nerve stimulation remains prevalent for SEP elicitation, because it provides a measurable range for event-related potentials (ERPs), preventing overlap of short stimuli and allowing for precisely timed events well-suited for electroencephalography (EEG). However, its ecological validity is limited as this approach non-specifically stimulates all nerve fibers, regardless of their functional specialization, activating pathways that convey different types of sensory information, such as discriminative touch, proprioception, and nociception.

Vibrotactile stimulations are commonly used to investigate both the functional organization of touch and somatosensation using functional magnetic resonance imaging (fMRI) and the anatomical features using structural MRI (Ambron et al., 2024; Rabe et al., 2023), and temporal order judgement and tactile remapping in behavioral studies (S. Badde, Heed, et al., 2014, 2014). These stimuli are favored in behavioral research due to their ease of use in perception tasks (Fagius and Wahren, 1981; Klöcker et al., 2016), effectiveness in localizing touch and tactile discrimination (S. Badde, Heed, et al., 2014, 2014; Heed et al., 2012; Shore et al., 2002; Yamamoto and Kitazawa, 2001), and their long-lasting mechanical vibrations (up to 200 ms). Although vibrotactile stimuli are widely used in behavioral and fMRI research, their neurophysiological impacts—especially with respect to finger stimulation—remain poorly understood. Specifically, vibrotactile stimuli may activate mechanoreceptors in a more specific manner than electrical stimuli, which could result in distinct cortical activations. This specific activation could lead to new insights into the distinct neural processing of touch depending on stimulus modality, offering potential clinical applications in sensory rehabilitation. Unlike electrical or pressure-based stimuli, vibrotactile stimulation engages multiple mechanoreceptors in a frequency-dependent manner, allowing precise frequency and intensity discrimination while preserving ecological validity. It also provides finer control over stimulus parameters, essential for dose-response analyses, and reduces sensory habituation through randomized patterns. Furthermore, vibrotactile SEPs remain underexplored despite their potential to reveal distinct cortical processing mechanisms. Investigating these responses can refine our understanding of somatosensory integration and connectivity, bridging the gap between behavioral and neurophysiological studies.

Recent advancements in mechanical vibrotactile stimulation methods have targeted specific mechanoreceptors to study the neurophysiological processes of touch (Kalberlah et al., 2013; Kim et al., 2014; Klöcker et al., 2016; Rabe et al., 2023); slowly-adapting (SA; Merkel disks and Ruffini corpuscles), and rapidly-adapting (RA; Meissner corpuscles and Pacinian corpuscles) fibers. Specifically, Meissner corpuscles are optimal for detecting medium-frequency vibrations (around 50 Hz), Pacinian corpuscles are most sensitive to high-frequency vibrations (around 250 Hz), and Merkel disks are sensitive to sustained pressure, fine texture and low-frequency vibrations (around 10 Hz). Given that Meissner and Pacinian corpuscles are specialized in detecting low-frequency and high-frequency vibrations respectively (Abraira and Ginty, 2013; Feher, 2017; Johnson, 2002; Vallbo and Johansson, 1984), this raises the question of investigating receptor-specific cortical activations through mechanical stimulation. Discriminating between these mechanoreceptor responses is crucial for applications such as developing advanced prosthetic limbs that can provide a more nuanced and

realistic sense of touch by mimicking the specific frequencies these receptors are sensitive to (Ackerley and Kavounoudias, 2015). fMRI imaging studies have highlighted distinct cortical activations from RA and SA afferents. Meissner corpuscles project contralaterally to the primary somatosensory cortex (S1) and bilaterally to the secondary somatosensory cortex (S2), while Pacinian corpuscles primarily project to bilateral S2 (Chung et al., 2013). This suggests that the lateral sulcus of S2 might play a key role in processing vibrotactile frequency information. Vibrotactile signals preferentially activate these regions, suggesting distinct patterns of functional connectivity during the processing of haptic information (Rostamian et al., 2022). Studying these connectivity dynamics across different stages of cortical processing would provide a better understanding of the underlying mechanisms of somatosensory perception, particularly how vibrotactile information is processed and integrated in the context of proprioceptive and kinaesthetic perception (Ryun et al., 2023). Since the 2000s, finger or hand stimulation has become widespread, enabling detailed characterization of human primary and secondary somatosensory cortices. These studies have yielded precise activation maps of finger organization (Asghar et al., 2023; Khalife et al., 2022; O'Neill et al., 2020; Schellekens et al., 2021), revealing sequential functional information transmission from S1 to S2 and associative areas (Kalberlah et al., 2013; Kim et al., 2014). As vibrotactile stimulation is primarily used in behavioral studies investigating tactile functions, it raises questions regarding neurophysiological differences with electrical stimulation. However, the limited literature, based on independent studies, suggests similar early postcentral activation occurring around P45/P50 when using either electrical or vibrotactile stimulators (Bolton and Staines, 2011; Desmedt and Cheron, 1980; Forschack et al., 2020; Josiassen et al., 1982; Reuter et al., 2014; Rigato et al., 2019).

Another application of SEPs is the reconstruction of the topographic organization of S1. It was initially demonstrated using intraoperative electrocorticography (ECOG) during neurosurgery and direct electrical cortical stimulation (Penfield and Boldrey, 1937; Penfield and Rasmussen, 1950; Woolsey et al., 1979). Nowadays, EEG source localization, widely used in research and in routine as a presurgical technique in focal epilepsy, continues to evolve as a valuable tool for investigating fine-scale somatotopy, the temporal dynamics of cortical activations, and functional interactions between brain regions. Its non-invasive nature and high temporal resolution make it particularly relevant for studying sensory processing and brain connectivity in both research and clinical contexts. It offers high temporal resolution, relatively good spatial resolution, enabling the generation of head and source models to solve the inverse problem of determining the location and strength of neural sources within the brain based on the electrical activity recorded with scalp electrodes (Asadzadeh et al., 2020; Jatoi and Kamel, 2017; Mahjoory et al., 2017; Michel, 2019; Michel and He, 2019). This source localization method has evolved into a rapid and non-invasive means of exploring somatotopy in humans with EEG and magnetoencephalography (MEG), specifically after bipolar electrical stimulation.

Respectively, pneumatically driven tactile stimulation and electrical stimulation on the thumb or the little finger, showed distinct cortical representations using source localizations (Braun et al., 2001; Houzé et al., 2011). Similar findings in MEG indicated significant separations of index and ring fingers, although some residual noise was noted (Jamali and Ross, 2012). A comparison of EEG and MEG systems during a pneumatic stimulation of the right index finger showed that EEG localization could be more accurate than MEG localization for the same number of channels (Klamer et al., 2015).

Nevertheless, recently, Kalogianni, Daffertshofer, et al. (2018) disentangled finger stimulation induced-SEPs robustness using EEG, and they found overlapping dipole representations, accompanied with variable results in finger somatotopic representation, suggesting no consistency in the findings. While these results were based on electrical or air pressure stimulation, there is a significant gap in the literature regarding source localization following mechanical vibrotactile
stimulation. Only a few neurophysiological studies have used a vibratory device instead of the more common electrical methods. Cheyne et al. (1998) demonstrated that both MEG and EEG early responses were reliable enough to map the somatotopy organization after vibration on the fingertips.

Nevertheless, despite the growing interest in use of vibrotactile stimulation of fingers for exploring sensory cortical activations, studies directly comparing SEP characteristics and cortical responses to electrical versus vibrotactile finger stimulation remain scarce.

In this study, we aim to compare cortical activations from electrical and vibrotactile stimulations of fingers using EEG. Since electrical stimulation is commonly used in EEG studies of somatosensory processing despite its limited ecological validity, while vibrotactile stimulation is prevalent in behavioral research, our study aims to provide a methodological framework to establish the spatiotemporal dynamics and connectivity patterns of these stimulations. By detailing these differences, we contribute to a better characterization of cortical somatosensory processing and refine methodological approaches for future neurophysiological studies. Stimulations were administered on the right hand's fingers: thumb (D1), index (D2), middle finger (D3), ring finger (D4), little finger (D5). The vibrotactile stimuli were delivered at frequencies that optimally activated the mechanoreceptors. Firstly, we compared the early somatosensory evoked potentials elicited after electrical and vibrotactile stimulation of the finger. Secondly, considering the limited literature specifically addressing finger stimulation, particularly with vibrotactile cues, and the evidence that vibrotactile signals engage distinct parietal secondary areas associated with proprioceptive and kinaesthetic processing, we also compared connectivity in those stages of cortical processing. Finally, we explored differences in early cortical source localizations computed with dipole fitting between electrical and vibrotactile stimulation of the fingers to investigate whether both types of stimulation can reliably reconstruct the somatotopic organization of the fingers.

## 2. Material & methods

### 2.1. Participants

We recruited forty-five volunteers with no history of neurological or psychiatric disorder or reported tactile deficit, divided in two groups who underwent two different experiments. The first group (experiment 1) was composed of twenty-two healthy volunteers (12 females, 10 males, mean age = 28.8 ± 8.3, 1 left-handed). The second group (experiment 2) was composed of twenty-three healthy volunteers, one was excluded due to a technical issue during the recording, resulting in a group of twenty-two healthy subjects (11 females, 11 males, mean age = 30.4 ± 7.5, all right-handed). All participants provided their written informed consent prior to the testing. The experimental protocol was approved by the institutional ethics committee of the Université Paris Cité (CER U-Paris Cité N° IRB: 00012022–54 & CER U-Paris Cité N° IRB: 00012023–46) and was in accordance with the ethical standards of the 1964 Declaration of Helsinki.

The sample size of our first experiment was determined using findings from a previous pilot study (See Pilot Study section and Table 1), and we maintained the same sample size for the second experiment. The

<caption>**Table 1**</caption>

Mean values and Pearson correlation coefficients of SEP amplitudes for each finger after electrical stimulation between timepoints 1 and 2.

<table><thead><tr><th>Condition</th><th>T1<br>Mean ± std (µV)</th><th>T2<br>Mean ± std (µV)</th><th>Pearson correlation</th></tr></thead><tbody><tr><td>D1 (thumb)</td><td>0.49 ± 0.16</td><td>0.55 ± 0.18</td><td>0.87</td></tr><tr><td>D2 (index)</td><td>0.51 ± 0.15</td><td>0.51 ± 0.07</td><td>0.89</td></tr><tr><td>D3 (middle finger)</td><td>0.51 ± 0.11</td><td>0.53 ± 0.08</td><td>0.87</td></tr><tr><td>D4 (ring finger)</td><td>0.52 ± 0.11</td><td>0.56 ± 0.09</td><td>0.93</td></tr><tr><td>D5 (little finger)</td><td>0.45 ± 0.06</td><td>0.54 ± 0.1</td><td>0.92</td></tr></tbody></table>

main criterion for determining the sample size was the average Euclidean distance between source locations (Biermann et al., 1998) at the P1 component for two adjacent fingers (D1 and D2) (Dandu et al., 2018; Hamada and Suzuki, 2005) versus two non-adjacent fingers (D1 and D5) (Jamali and Ross, 2012), based on their (x, y, z) coordinates (D1-D2: mean amplitude = 25.9 ± 17.6; D1-D5: mean amplitude = 17.6 ± 7.0). A priori power analysis was conducted using G*Power (paired-sample t-test, two-tailed) with α = 0.05, statistical power = 0.80, and an effect size of Cohen's dz = 0.62, as estimated from our pilot data. The analysis yielded a required sample size of 22.43, which we rounded down to n = 22 participants for both experiments.

A between-subject design was chosen from the outset due to practical constraints: each stimulation protocol lasted approximately 45 min, during which participants had to remain still with their eyes closed. Performing both stimulation types in a single session would have required nearly 90 min of continuous recording, increasing the risk of fatigue, falling asleep and habituation, thus reducing data quality. This justified the use of two separate experimental groups.

### 2.2. Experimental design and data acquisition

#### 2.2.1. Experiment 1: cortical activation during electrical finger stimulation

Electroencephalography data were acquired with a 64-channel electroencephalography system (ActiveTwo system from Biosemi, Amsterdam, The Netherlands), and a battery-powered electrical stimulator (ENERGY model from MICROMED, Treviso, Italy). This condition will hereafter be referred to as ELEC. Each subject was seated in a comfortable chair, eyes closed. The right hand was relaxed on the right armrest, and the left hand on the left armrest. Each participant wore an EEG cap plugged to the acquisition system, and a bipolar ring placed on the finger to be tested, wired to the electrical stimulator. The anode was placed at the most distal phalange, and the cathode at the most proximal phalange of the finger of the right hand (Fig. 1A, left panel). The tactile stimulus was a squared wave electrical pulse of 100 µs duration at a 3 Hz frequency, the intensity being at 1.5x the detection threshold (when possible), while ensuring comfortable stimulation in accordance with the subject's feelings. All impedances were below 5 kΩ, and the EEG signal was recorded at a 2048 Hz sampling rate. The experiment comprised five blocks, each block consisted in 1500 stimulation trials repeated at 3 Hz for each finger, corresponding to five hundred seconds per finger, with a resetting of stimulation intensity and a brief pause between blocks, for a total duration of 45 min experiment. All five fingers were stimulated in a pseudo-randomly order so that two neighboring fingers were not stimulated one after the other. Each participant from the electrical group underwent two timepoints (T1, T2) of EEG recording and finger stimulation a week apart, in order to study test-retest reliability of the methodology (Fig. 1B, top panel). This step is crucial to ensure that our analysis techniques are robust and reliable, as consistent results across multiple timepoints are essential for validating the reproducibility of our findings and the effectiveness of the methodology. Stimulation intensity was maintained constant (when possible) for both visits (mean intensity T1 = 8.28 ± 4.29; mean detection threshold T1 = 5.52 ± 2.86; mean intensity T2 = 8.09 ± 4.40; mean detection threshold T2 = 5.39 ± 2.93; Supp Table S1), with the detection threshold evaluated for each finger, and the stimulation intensity adjusted accordingly for each block.

#### 2.2.2. Experiment 2: cortical activation during vibrotactile finger stimulation

Neurophysiological data were recorded using the same setup as experiment 1, except that the stimulation was delivered using a small speaker mounted as a vibrotactile stimulator (GD04 speaker from Heltec Automation, China), controlled by an Arduino electronic card (Arduino Mega 2560 board, Ivrea, Italy). This condition will hereafter be referred to as VIBR. Vibrotactile stimulations were applied at each finger pad of the right hand (Fig. 1A, right panel). Three vibration frequencies were
<figure><img src="image_2.png" /><figcaption>**Fig. 1.** Experimental design for the two experiments. A) (Left panel) Experiment 1 consisted in electrical stimulations (ELEC) to each finger, which were conducted using a bipolar system consisting of anodal and cathodal rings on the first two phalanges. Electrical stimulation of the right index finger (D2) is depicted. (Right panel) Experiment 2 consisted in vibrotactile stimulations (VIBR) on the finger pad, and electrical stimulations on the right index finger. The example depicts vibrotactile and electrical stimulations of the right index finger. B) (Top panel) Experiment 1 entailed two sessions with 1500 electrical stimulations applied pseudo-randomly to the five fingers, for timepoints 1 (T1) and 2 (T2). (Bottom panel) Experiment 2 comprised a single session of 500 electrical stimulation of the index, and 500 vibrotactile stimulations of the five fingers, administered pseudo-randomly, under three different frequencies (10, 50 and 250 Hz), drawn randomly and kept constant across fingers.</figcaption></figure>

studied, corresponding to a square wave of 10, 50 and 250 Hz fre-
quencies with 10 ms duration. The 10 ms duration refers to the pulse
width of the square wave, ensuring a consistent stimulus across fre-
quencies. The intensity was modulated using an amplifier (amplifier
board from Dioche, China), with an inter-stimulus interval of 333 ms.
Intensities of stimulation were determined in a pilot study using the
Staircase Procedure for Finding a Perceptual Threshold (Cornsweet,
1962) and modulated individually if needed (Supp Table S2). The
amplification of stimulation during the different frequency conditions
was significantly different (F(2,42)=251.647, p<.001; Supp Table S3)
between 10 and 250 Hz and between 10 and 50 Hz (10–50 Hz: p<.001;
10–250 Hz: p<.001; 50–250 Hz: p=.475) (Supp Table S4). The three
frequencies correspond to three stimulation conditions randomized be-
tween them, and pseudo-randomized across fingers, so that two neigh-
boring fingers were not stimulated one after the other, to avoid
overstimulating the same nervous fibers. Each block consisted in 500
stimulation trials for each finger at each frequency, for a total duration
of 45 min experiment. Each participant from the vibrotactile group
received vibrotactile stimulations, and a single block of electrical stim-
ulation to the index finger (again with 500 repetitions), in order to
directly compare the two types of stimulation in the same individuals
(Fig. 1B, bottom panel). This design ensured that participants from both
groups were exposed to both types of stimulation (electrical and
vibrotactile), allowing for a direct and reliable comparison between the

two conditions. The stimuli frequency and duration were the same as the
experiment 1 in the electrical stimulation group. All recordings were
done in a single session.

## 2.3. *Data pre-processing*

Preprocessing of neurophysiological data of both experiments was
done to remove possible artifact contamination, by using MNE-Python
(Gramfort et al., 2013) library running under Python. Signals were
first referenced to the mastoids, then high-pass filtered at 0.1 Hz and
low-pass filtered at 50 Hz. We removed bad channels (i.e. the noisy ones;
1.9 % for experiment 1, 0.4 % for experiment 2), using statistical mea-
sures such as detecting channels with unusually high or low signal
variance, extreme values (e.g., signal saturation or flatlines), and poor
correlation with adjacent channels. Additionally, we performed a visual
inspection of signals for each electrode and analyzed their spectral
properties to identify channels exhibiting abnormal frequency content
or noise characteristics. We re-referenced to the average, the advantage
being that, following Ohm's law, outward positive and negative cur-
rents, summed across an entire electrically isolated sphere, will sum to 0.
An ICA was used (Independent Component Analysis) to detect and
correct for common artifacts (eye movements, eye blinks, muscular
contractions and cardiac pulses) on a 1–50 Hz band-pass filter, to
remove the baseline drift. This process showed good results in terms of
signal-to-noise ratio (SNR), single-trial classification accuracy and the percentage of near-dipolar ICA components (Winkler et al., 2015). The resulting calculated ICA weight matrix was applied to our 0.1–50 Hz data. We interpolated the bad electrodes which were excluded in a previous step. Finally, we marked bad epochs, i.e., those with excessive peak-to-peak amplitudes above 150 µV or flat signals below the threshold (≈ 0.98 % epochs rejected in experiment 1, resulting in 1485.3 trials per condition on average, and 1.08 % epochs rejected in experiment 2, resulting in 494.6 trials per frequency on average) and applied a baseline correction to get our final cleaned somatosensory evoked potentials on a [−100, 300] ms time window. Although the very beginning of the baseline period may slightly overlap with the tail end of the preceding SEP, the baseline correction was applied over the entire pre-stimulus interval (−100 to 0 ms). This minimal overlap does not affect the early or late components of the SEP time-locked to the current stimulus, nor does it introduce bias into the analysis (Alday, 2019; Wang et al., 2008; Woldorff, 1993; Zhang et al., 2024).

## 2.4. Source localization

### 2.4.1. Computation

Source localizations using eLORETA (exact Low Resolution brain Electromagnetic Tomography) were computed as source estimates containing time series of activations at spatial locations. This approach allowed us to estimate the neural generators underlying the EEG signals recorded at the scalp by solving the inverse problem, which is inherently ill-posed. eLORETA provides a distributed solution by estimating thousands of potential sources across the entire brain volume. To solve the inverse model, a forward model of the head must be created in order to measure how the electric fields spread through it, and calculate exact three-dimensional coordinates of the source. We used an MNI brain (152 averaged T1-weighted stereotaxic volumes from the Montreal Neurological Institute), in order to create a boundary element method (BEM), which uses triangulations of the interfaces between the three compartments (scalp, skull, cortex) of equal isotropic conductivities as a geometric model. Based on the montage and the position of each electrode in the space, we then normalized each individual data to the MNI template. To complete source localization, we used the LORETA method (Dattola et al., 2020; Pascual-Marqui, 2002; Pascual-Marqui et al., 2018, 2011), which uses thousands of equivalent dipoles in the brain volume (Scherg et al., 2019). eLORETA provided a spatially distributed estimation of cortical activity, allowing us to extract functional time series from predefined anatomical regions. These source estimations were then used to extract region-specific time series, which served as the basis for subsequent functional connectivity analyses (see Connectivity section).

### 2.4.2. Connectivity

All-to-all functional connectivity analysis was conducted on smaller epochs extracted from the previously defined 400-ms-long epochs (from −100 ms to 300 ms after stimulus onset). Specifically, we selected 50-ms-long epochs around the peaks of interest that were identified in the SEPs analysis. These peaks were determined based on the waveform characteristics (such as the latency and amplitude of specific components). Temporal series were then extracted for each region of interest (ROI) defined by a modified-Desikan-Killiany Atlas cortical parcellation consisting of 40 ROIs (Desikan et al., 2006). These series captured the average brain source activity within each ROI over time and were derived from eLORETA-reconstructed cortical activity. Spectral connectivity was subsequently computed between these ROIs, assessing the synchronization across various frequency bands (0–50 Hz) using debiased Weighted Phase Lag Index (debiased estimator of squared WPLI). This broadband approach was chosen due to the absence of strong a priori hypotheses about relevant frequency bands, given the passive nature of the task (somatosensory stimulation with eyes closed) which does not reliably engage specific oscillatory rhythms such as alpha or beta. It allowed for an unbiased assessment of global

synchronization (Candelaria-Cook and Stephen, 2020; Pourmotabbed et al., 2022), and exploratory analyses in narrower alpha and beta bands yielded similar patterns, supporting the robustness of our findings. Moreover, the use of the multitaper method ensured stable phase estimates while avoiding spectral smearing (Prieto et al., 2007). For the sake of readability, we have plotted the strongest 25 connections. Differences between conditions based on two ROIs were investigated using Kruskal-Wallis test and Mann-Whitney U post-hoc tests. Statistically significant difference was set at $p \le 0.05$ before correction for multiple comparison following FDR correction. We opted for this statistical analysis approach because EEG data is multidimensional, leading to the challenge of addressing the multiple comparisons problem. The signals are sampled across various channels and cropped time segments, creating a spatiotemporal structure involving multiple (channel, time) pairs. The dWPLI values were computed from these specific time windows, which distinguishes this approach from conventional statistical methods that typically work with individual (channel, time) pairs (Maris and Oostenveld, 2007). Outliers from source localization data were rejected using a z-score with a threshold score of 3.

## 2.5. Dipole fitting

### 2.5.1. Computation

Dipole fitting using ECD (Equivalent Current Dipole) was computed as the dipole vectors given in the head coordinate frame with the best goodness of fit (GOF), for the time window corresponding to our peak of interest (P1). The ECD model assumes that neural activity can be approximated by a small number of focal source. To complete dipole fitting, we used the ECD model, which assume that signals are due to a single source vectorized as a dipole (Wendel et al., 2009). The ECD approach was used for identifying focal sources with high temporal precision. ECD analysis was conducted using a set of 20 electrodes (Bai and He, 2005; Scherg et al., 2019) arranged in a rectangular configuration over the cortical area of interest (FT7, FC5, FC3, FC1, FCz, Cz, C1, C3, C5, T7, TP7, CP5, CP3, CP1, CPz, Pz, P1, P3, P5, P7), targeting the representation of the right hand in the S1 homunculus of the left hemisphere. This localization was crucial for isolating the primary sensory response to tactile stimulation from other cortical processes. Dipoles with a GOF $\ge 70\%$ were retained for further analysis (Hartmann et al., 2018; Nevalainen et al., 2012; Pitkanen et al., 2019), and were localized using the ICBM 152 Nonlinear atlases version 2009 (RRID: SCR_008796) (Fonov et al., 2011, 2009).

### 2.5.2. Reliability of dipoles

In order to investigate the accuracy and the reliability of the current dipole obtained from the electroencephalographic neural activity, we used a resampling bootstrapping technique (Efron, 1979; Jamali and Ross, 2012; Oruç et al., 2011; Parks et al., 2016). Bootstrapping consists of drawing random sub-samples and repeating the sampling a large number of times to provide information on the variability of the estimator and to define confidence limits. Indeed, bootstrap resamples were generated by sampling with replacement from the epoch sets and averaging (Efron and Tibshirani, 1986, 1993; Johnson, 2001). This approach offers the advantage of managing small samples and that no specific assumptions are made regarding the distribution of the noise, the dipole time series, or the number of dipoles (Darvas et al., 2005; Di Nocera and Ferlazzo, 2000). We randomly sampled 1500 trials with replacement, and constructed a bootstrap of the average response based on the MNI brain normalized with Talairach coordinates (Lancaster et al., 2007; Mazziotha et al., 1995; Talairach and Tournoux, 1988). For each dataset of 1500 trials in each individual separately, a dipole was computed using the ECD model on a set of 20 electrodes, as described in Source Localization and Dipole Fitting section, resulting in 100 dipoles for each condition, where every dipole was depicted in 3D space in (x, y, z) coordinates (Kalogianni, de Munck, et al., 2018). Statistical data were computed with Kruskal-Wallis tests and Dunn post-hoc tests. To assess
the reliability of our measures from T1 and T2, the Euclidean distance between the dipole and the origin (the center of the head model) was computed from the x, y, and z coordinates of each dipole. The intraclass correlation and the linear regression were quantified between the distance values from T1 and T2.

## 2.6. Statistical analysis

Statistical analyses were conducted separately for stimulation intensity parameters, SEPs, and eLORETA source data. For stimulation intensity, a two-way repeated-measures ANOVA was performed with stimulation intensity as the dependent variable. For electrical stimulation, the independent variables were timepoint (T1, T2) and finger (D1-D5), while for vibrotactile stimulation, the independent variables were finger (D1-D5) and frequency (10 Hz, 50 Hz, 250 Hz). Tukey's HSD post hoc tests were applied to significant effects. We performed a Kruskal-Wallis test to compare the amplitude of the SEP (dependent variable) between two independent groups (electrical vs. vibrotactile stimulation) (independent variable) across the whole SEP and specific peaks (P1, N1, P2). SEP amplitudes and latencies within a single stimulation modality (electrical or vibrotactile) were analyzed using paired t-tests. To compare SEP responses across stimulation modalities (electrical vs. vibrotactile), separate ANOVAs were conducted for each peak and each finger, with condition (ELEC, VIBR 10 Hz, VIBR 50 Hz, VIBR 250 Hz) as the independent variable and amplitude or latency as the dependent variable. Post hoc comparisons were performed using *t*-tests. For eLORETA source data, a repeated-measures ANOVA was conducted with condition as the independent variable and source activity at anatomical control points as the dependent variable. Tukey's HSD post hoc tests were applied to significant effects. Multiple comparisons were corrected using the false discovery rate (FDR) method.

## 2.7. Pilot study

We conducted a pilot study for experiment 1 in a separate sample of controls to collect preliminary data on the feasibility and reliability of the electrical stimulation protocol. The main objectives were to assess the robustness of the stimulation procedure, fine-tune the experimental setup, and ensure the stimulus intensity was appropriate for the main study. This pilot study allowed us to evaluate the quality of the recorded signals, identify any technical issues with the stimulation, and ensure that the procedure was well-tolerated by participants. The data obtained also helped in estimating the expected variability and informed the design of the main experiment (Supp Table S5). Eight volunteers were recruited, one was excluded due to noisy signals, for a total of seven participants (three females, four males, mean age ± standard deviation = 30.0 ± 10.1 years, all right-handed). Six participants out of seven underwent the two sessions of recording and stimulation. The procedure and the material were the same as the group in experiment 1, except that each block consisted in 1800 stimulations. Frequency was 3 Hz and duration 100 µs, the intensity being at 1.5x the detection threshold (when possible), avoiding painful and uncomfortable stimulation intensity in accordance with the subject's feelings, as in the main study.

# 3. Results

## 3.1. Test-retest of SEPs and dipole fitting

In order to assess the reliability of the preprocessing and the analysis procedures after finger stimulation recorded through EEG, we used a test-retest paradigm on electrical stimulation data from experiment 1 (Näpflin et al., 2007; Pang and Mueller, 2014; Rentzsch et al., 2008; Suárez-Revelo et al., 2015). Pearson's correlation analysis revealed that SEPs amplitude at the first positive peak were strongly correlated for each finger across T1 and T2 with Pearson correlation coefficient (PCC) values higher than 0.87 (Table 1). Intraclass correlation analysis for

each finger showed strong correlations in amplitude between values at T1 and T2 with intraclass correlation (ICC) values higher than 0.8 (D1: ICC3k=0.89, F(63,63)=8.95, *p*<.001; D2: ICC3k=0.82, F(63,63)=5.56, *p*<.001; D3: ICC3k=0.81, F(63,63)=5.14, *p*<.001; D4: ICC3k=0.8, F(63, 63)=4.97, *p*<.001; D5: ICC3k=0.87, F(63,63)=7.97, *p*<.001).

There was no difference in stimulation intensity between the recordings at T1 and T2 (F(1,21)=0.012, *p*=.912; Supp Table S6). To assess the test-retest reliability of the dipole fitting, we computed the Euclidian distance from the origin to each three-dimensional dipole, resulting in a distance variable. The ICC analysis for dipole fitting for average fixed raters (ICC3k) yielded an ICC of 0.828, indicating good reliability, with an F-statistic of 5.812 (df1=21, df2=21, *p*<.001), and a 95 % confidence interval of [0.59, 0.93]. This suggests that the measure is consistently applied across timepoints for the dipoles representing each finger, with 82.8 % of the variance in measure attributable to true differences between subjects. The Bland-Altman analysis revealed a mean difference of 3.54 with 18 values in the central zone and 4 values outside the ±1.96 SD limits (Fig. 2A). Additionally, the linear regression analysis showed a significant correlation between T1 and T2 measurements (R²=0.52, *p*<.001) (Fig. 2B).

## 3.2. Cortical activation during electrical finger stimulation (Experiment 1)

Somatosensory evoked potentials elicited from the five fingers after electrical stimulation and plotted at C3-CP3 electrodes showed a strong positive deflection at 42.43 ± 6.34 ms (P1), with no statistical difference between digits (all |t|(21)<1.94, *p*>.31) (Fig. 3A), after *t*-test and FDR correction for multiple comparison. We identified two other peaks with early latencies: a negative peak at 59.96 ± 5.79 ms (N1) (all |t|(21)< 1.47, *p*.40 between digits), and a positive peak at 70.46 ± 7.80 ms (P2) (all |t|(21)<3.90, *p*.18 between digits).

The topographical maps illustrate the cortical activations at each peak, highlighting a strong positive voltage above the centro-parietal cortex at 45 ms, which projects to the posterior cortices over time (Fig. 3B).

## 3.3. Cortical activation during vibrotactile finger stimulation (Experiment 2)

Somatosensory evoked potentials on C3-CP3 following vibrotactile stimulation showed consistent waveforms across different frequencies (10, 50 and 250 Hz) for all digits with a strong positive peak at around 50 ms (P1: 10 Hz: 49.46 ± 7.33, 50 Hz: 48.39 ± 6.87, 250 Hz: 48.44 ± 6.82), followed by a negative peak around 70 ms (N1: 10 Hz: 78.32 ± 7.43, 50 Hz: 72.41 ± 7.72, 250 Hz: 69.82 ± 7.26), and a second positive peak at 100 ms (P2: 10 Hz: 104.15 ± 7.04, 50 Hz: 97.95 ± 6.57, 250 Hz: 93.60 ± 7.07) (Fig. 4A-C). Topographical maps of merged digits show a strong activation at 50 ms above the centro-parietal cortex for all frequencies (Fig. 4D-F). At P1, there was no significant difference in amplitude (Fig. 4G) (all |t|(21)<4.83, *p*.12 for all paired *t*-test comparisons between frequencies for each finger) or latency (Fig. 4H) (all |t|(21)<3.0, *p*.15) after FDR correction for multiple comparisons.

## 3.4. Comparison of early cortical activations: electrical versus vibrotactile finger stimulation

Beforehand, we controlled for potential group differences by comparing the SEPs elicited from electrical stimulation of D2 in both experiment 1 and experiment 2. Our analysis showed no significant difference across the whole ERP (H(1)=0.856, *p*=.355) nor in specific time windows around identified peaks (P1: H(1)=2.118, *p*=.146; N1: H(1)=0.986, *p*=.321; P2: H(1)=0.082, *p*=.775). This confirmed that the two groups did not significantly differ in their responses to the electrical stimulation, allowing us to confidently compare the two groups (electrical vs. vibrotactile) within the same experimental framework.
A

<figure><img src="image_3.png" /><figcaption>Fig. 2. Test-retest electrical simulation results after dipole fitting. Three-dimensional coordinates extracted from bootstrapped dipoles and computed as the Euclidian distance from the origin. A) Bland-Altman plot of the mean distance compared to the difference in distance at first (T1) and second (T2) test day. The differences in distance between test days are relatively small and within acceptable limits of agreement, indicating good test-retest reliability. B) Linear regression of the mean Euclidian distance from the origin at T1 and T2. Significant positive correlation between the mean Euclidean distances at T1 and T2, suggesting consistency in the localization of the electrical source across test sessions.</figcaption></figure>

B

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

A

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

B

<figure><img src="image_6.png" /><figcaption>Fig. 3. Electrical cortical activations. A) Grand-average somatosensory evoked potentials across all participants from Experiment 1, elicited by the electrical stimulation of the five fingers. SEPs were averaged over all trials and recorded at C3-CP3 electrodes. Each trace represents the mean response for a given finger, with different colors corresponding to different fingers. Peaks of interest are indicated. B) Corresponding topographical maps per finger at each peak latency.</figcaption></figure>

SEPs for both electrical and vibrotactile stimulations revealed a similar general pattern of activation on C3-CP3, highlighting three main peaks at P1, N1 and P2 (Fig. 5A-E). ANOVA shows a significant effect of stimulation type on peak amplitude for each fingers at N1 and P2 (all |F|(3,84)>9.44, p<.03), but only for D4 at P1 (F(3,84)=14.36, p=.01). Post-hoc t-tests revealed three comparisons that survived correction for multiple comparisons at N1 and P2 (N1 D1 ELEC vs. VIBR 50 Hz: t(42)=46.00, p=.02; P2 D2 ELEC vs. VIBR 10 Hz: t(42)=69.24, p=.02; P2 D2 ELEC vs. VIBR 50 Hz: t(42)=48.72, p=.02).

Peak latencies were significantly affected by stimulation type using ANOVA for fingers D2, D3 and D4 at P1 (D2: F(4,105)=12.54, p=.008; D3: F(3,84)=17.84, p=.009; D4: F(3,84)=23.96, p=.005) and all fingers at N1 and P2 (all |F|(3,84)>8.98, p<.02). Significant post-hoc t-test comparisons revealed significant differences between stimulation types at P1 for D2, D3 and D4 (D2: t(42)=12.54, p=.008; D3: t(42)=17.84, p=.009; D4: t(42)=23.96, p=.005), and for all fingers at N1 and P2 (all |t|(42)>8.98, p<.02) (Fig. 5F-H). Overall, VIBR peaks latencies were significantly delayed compared to ELEC condition (P1 ELEC: 42.4 ± 6.3 ms; VIBR 10 Hz: 49.5 ± 7.3; VIBR 50 Hz: 48.4 ± 6.9; VIBR 250 Hz: 48.4 ± 6.8; N1 ELEC: 60.0 ± 5.8; VIBR 10 Hz: 78.3 ± 7.4; VIBR 50 Hz: 72.4 ± 7.7; VIBR 250 Hz: 69.8 ± 7.3; P2 ELEC: 70.5 ± 7.8; VIBR 10 Hz: 104.2 ± 7.0; VIBR 50 Hz: 97.9 ± 6.6; VIBR 250 Hz: 93.6 ± 7.1).

### 3.5. Cortical connectivity: electrical versus vibrotactile finger stimulation

Cortical connectivity was first computed for electrical and vibrotactile stimulations at all frequencies and each peak (strongest 25 connections; see Fig. 6A). Similar connectograms were observed between all vibrotactile frequencies, which remained consistent over time at P1, N1 and P2, displaying for instance strong connections, such as left orbitofrontal cortex (OFC)-left posterior cingulate cortex (PCC), left paracentral lobule (PC)-left S1 and right precuneus (PCun)-right superior parietal lobule (SPL). Both vibrotactile and electrical stimulations predominantly showed strong connectivity between the left PC and left parietal cortex. Unlike the VIBR conditions, where a strong fronto-cingulate connection was observed at P1, N1, and P2, no such connection was found in the ELEC condition.

Since similar patterns were found for all frequencies of VIBR condition, statistical comparison in connectivity were done on ELEC stimulation versus VIBR 250 Hz. Statistically significant differences in ROI connectivity between ELEC and VIBR 250 Hz were observed at all three peaks (P1, N1, P2), after Kruskal-Wallis test and Mann-Whitney U post-
<figure><img src="image_7.png" /><figcaption>**Fig. 4.** Vibrotactile cortical activations. Grand-average somatosensory evoked potentials across all participants from Experiment 2, elicited by the vibrotactile stimulation of the five fingers at three different frequencies: A) 10 Hz (light colors), B) 50 Hz (mid-tone colors), and C) 250 Hz (dark colors). SEPs were averaged over all trials and recorded at C3-CP3 electrodes. Each trace represents the mean response for a given finger, with different colors corresponding to different fingers. Peaks of interest are indicated. Corresponding topographical maps at each peak latency, all finger being merged, for low D), medium E) and high F) frequencies. G) Amplitude and H) latency at P1. Similar amplitudes and latencies are observed across fingers in response to vibrotactile stimulation at the three frequencies.</figcaption></figure>

hoc tests (Fig. 6B, Supp Table S7). At P1, the majority of differences (12 out of 13) favored the VIBR condition, showing greater connectivity, particularly in regions such as the SPL, insula, and inferior temporal gyrus (ITG) (Fig. 6C). At N1, 13 out of 16 significant differences again highlighted stronger connectivity in the VIBR condition, involving areas such as the superior frontal gyrus (SFG) and inferior frontal gyrus (IFG). However, at P2, a larger number of significant differences (66 in total) was found, with connectivity primarily involving the left SFG and right SPL, showing increased connectivity in the ELEC condition, particularly in inter-hemispheric connections with regions like the right primary motor cortex (M1), left and right SPL, and right superior temporal gyrus (STG).

### 3.6. Source localization time course: electrical versus vibrotactile finger stimulation

Source localization at P1 revealed a similar pattern of positive (red) and negative (blue) activation over S1 on the postcentral gyrus (Fig. 7), with no significant difference between Control Points—anatomical reference markers used for source localization—comparing ELEC and all VIBR conditions in the ANOVA ($F(3,84)=2.19, p=.086$) (Supp Table S8). However, significant differences were observed between ELEC and VIBR at all frequencies at N1 and P2 (N1: $F(3,84)=16.22, p<.001$; P2: $F(3,84)=12.12, p<.001$), as shown with Tukey's HSD post hoc test (N1 ELEC vs. VIBR 10, 50 and 250 Hz: $p<.001$; N1 VIBR 10 vs. 50 vs. 250 Hz: $p>.9$; P2 ELEC vs. VIBR 10, 50 and 250 Hz: $p<.001$; P2 VIBR 10 vs. 50 vs. 250 Hz: $p>.9$) (Supp Table S9). At P2, sources elicited by the electrical stimulation were localized over somatosensory cortices, whereas sources following 10 Hz vibrotactile stimulation were found in the middle temporal gyrus.

### 3.7. Somatotopic reconstruction using dipole fitting: electrical versus vibrotactile finger stimulation

For each condition and finger at P1, we computed a dipole corresponding to the source localization, retaining only those with a GOF $\ge$ 70 %. This resulted in 55 % of dipoles with a good fit for ELEC, 19 % for VIBR 10 Hz, 24 % for VIBR 50 Hz, and 40 % for VIBR 250 Hz. Electrical GOF were significantly higher than all other conditions (all $p<.031$). 250 Hz vibrotactile showed significantly best GOF than 10 Hz ($p=.003$), but 50 Hz vibrotactile did not differ from other vibrotactile conditions (all $p>.067$).

Kruskal-Wallis tests for each finger revealed no significant differences in the $(x,y,z)$ coordinates of the dipoles across conditions (all $|H| (3)<6.264, p>.099$) (Table 2). Anatomical regions were identified by entering our MNI coordinates (Table 2) into the ICBM 152 Nonlinear Atlas. Based on these atlas coordinates, electrical stimulation produced dipoles identified over the Brodmann area 3a of the postcentral gyrus (S1), vibrotactile stimulations at 50 and 250 Hz were located over the Brodmann areas 3b and 3a of S1, whereas vibrotactile at 10 Hz resulted in more posterior dipole locations, primarily in the intraparietal sulcus (IPS) and the superior parietal lobule (SPL) (Fig. 8A).

A visual inspection of the computed dipoles suggested that ELEC followed the expected anatomical somatotopic organization, with D1 positioned more inferiorly and laterally, while D5 was located more superiorly and medially. The other fingers—ring, middle, and index—appeared in overlapping positions, intermediate between these two points. For VIBR 250 Hz, a similar organization was visually observed, with D1 correctly positioned and substantial overlap among D2, D3, and D4. However, D5 appeared more inferior than expected. In contrast, for VIBR 10 Hz and 50 Hz, the expected somatotopic organization was less apparent (Fig. 8B).
<figure><img src="image_8.png" /><figcaption>Fig. 5. SEPs after Electrical and Vibrotactile stimulation per finger. SEPs plotted on C3-CP3 electrodes for A) D1, B) D2, C) D3, D) D4, and E) D5. Grey zones correspond to differences in latency at the maxima of each peak. Peaks of interest are indicated. Differences in latency between electrical and vibrotactile stimulation at F) P1, G) N1, and H) P2. Significant differences in latency were found between ELEC and VIBR at P1 for D3 and D4, and for all fingers at N1 and P2.</figcaption></figure>

We compared the three-dimensional coordinates in MNI space obtained from our data with reference data from the literature, based on a review of 95 fMRI studies investigating tactile finger stimulation (Holmes and Tamé, 2019). From dipoles with GOF ≥ 70 %, we computed the mean vector between each finger location and the corresponding reference data for each condition (Table 3). The vector represents the spatial deviation or shift of each finger (x, y, z coordinates) from the reference, while its norm quantifies the magnitude of the difference between our data and the reference data, providing a quantification of the deviation from the reference. These two metrics captures both the absolute positioning of the fingers in space and their alignment with the reference coordinates. Overall, the data in the 10 Hz vibrotactile condition was the farthest from the reference (27.5 mm), with a deviation mainly negatively located in the y axis. The closest dipoles from the reference were 250 Hz (12.7 mm), closely followed by 50 Hz (13.4 mm) and electrical stimulation (14.4 mm), all mostly positively deviated on the x axis.

# 4. Discussion

## 4.1. Main findings

Electrical and vibrotactile finger stimulations produced somatosensory evoked responses with distinct characteristics. We identified the

first three main peaks (P1, N1, P2) as the initial consecutive peaks in each condition (Cheng et al., 2010; Kwast-Rabben et al., 2002). The presence and the latencies of the three main peaks P1, N1, and P2 after electrical and vibrotactile stimulation of the fingers were consistent with classical descriptions of SEPs reported for primary somatosensory responses (Bolton and Staines, 2011; Calmes and Cracco, 1971; Cheyne et al., 1998; Guidotti et al., 2023; Josiassen et al., 1982; Reuter et al., 2014; Rigato et al., 2019). Early responses, measured by the P1 peak, showed slightly shorter latencies with electrical stimulation compared with vibrotactile stimulation. Later peaks revealed significant increasing difference in latency at N1, and P2, where potentials to electrical stimulation were elicited sooner than vibrotactile ones. Electrical stimulations induced responses with higher amplitudes and more extensive connectivity compared to vibrotactile stimulations. Connectivity between different cortical regions revealed distinct patterns, with vibrotactile stimulations showing more localized early connectivity at P1 and N1, while electrical stimulations exhibited wider connectivity at P2, particularly involving the right superior parietal lobule and the left superior frontal gyrus. Source dipoles and reconstructions at P1 revealed different localizations for low frequency vibrotactile (10 Hz) finger stimulation versus electrical and high frequency vibrotactile (50 Hz, 250 Hz), with these latter being more accurately localized to the postcentral gyrus, while 10 Hz vibrotactile were more dispersed. Moreover, vibrotactile dipoles with a high goodness of fit were in a smaller number in
<figure><img src="image_9.png" /><figcaption>**Fig. 6.** Connectivity analysis between Electrical and 250 Hz Vibrotactile. A) Connectivity connectograms depicting the top-25 connections, all fingers merged. B) Significant connections between two ROIs plotted by peak according to their level of significance after Kruskal-Wallis test. C) Heatmap showing all significant connections based on the Mann-Whitney U post-hoc tests. Green means higher connectivity in ELEC condition and purple means higher connectivity in 250 Hz VIBR condition. Level of significance was set at *p* ≤ 0.05. *Intra-Lh* = intra-left hemisphere, *Intra-Rh* = intra-right hemisphere, *Inter-h* = inter-hemispheres. *OFC* = orbito-frontal cortex, *IFG* = inferior frontal gyrus, *M1* = primary motor cortex (precentral gyrus), *MFG* = middle frontal gyrus, *SFG* = superior frontal gyrus, *PC* = paracentral gyrus, *ITG* = inferior temporal gyrus, *PHC* = parahippocampal cortex, *STG* = superior temporal gyrus, *IPL* = inferior parietal lobule, *S1* = primary somatosensory cortex (postcentral gyrus), *PCun* = precuneus, *SPL* = superior parietal lobule, *Cun* = cuneus, *LOG* = lateral occipital gyrus, *Calc* = calcarine cortex, *ICC* = isthmus cingulate cortex, *PCC* = posterior cingulate cortex, *ACC* = anterior cingulate cortex, *Ins* = insula</figcaption></figure>
<figure><img src="image_10.png" /><figcaption>**Fig. 7.** eLORETA source estimates from Electrical and Vibrotactile conditions at all frequencies, all finger merged, per peak. Red activation means positive activity, blue activation means negative activity. At P1, source localizations show no significant differences between conditions. 10, 50 and 250 Hz VIBR sources are significantly different from ELEC at N1 and P2, but not VIBR between each other. These source estimates are derived from EEG data. The MNI template is used solely for anatomical reference.</figcaption></figure>

<caption>**Table 2**</caption>

**MNI coordinates from ECD dipoles at P1, comparing Electrical and Vibrotactile stimulations.** Dipoles with a GOF ≥ 70 % were averaged, giving the (x,y,z) mean MNI coordinates for each finger. Reference data (Holmes and Tamè, 2019) are depicted on the last column. Results are based on the Kruskal-Wallis test, with the significance level set at *p* ≤ 0.05. Coordinates are presented in millimeters (mm).

<table><thead><tr><th rowspan="2"></th><th colspan="3">VIBR 10 Hz</th><th colspan="3">VIBR 50 Hz</th><th colspan="3">VIBR 250 Hz</th><th colspan="3">ELEC</th><th rowspan="2">F</th><th rowspan="2">p</th><th colspan="3">Ref.</th></tr><tr><th>x</th><th>y</th><th>z</th><th>x</th><th>y</th><th>z</th><th>x</th><th>y</th><th>z</th><th>x</th><th>y</th><th>z</th><th>x</th><th>y</th><th>z</th></tr></thead><tbody><tr><td>D1</td><td>-38</td><td>-56</td><td>33</td><td>-35</td><td>-30</td><td>38</td><td>-33</td><td>-23</td><td>41</td><td>-30</td><td>-26</td><td>49</td><td>6.264</td><td>.099</td><td>-48</td><td>-19</td><td>47</td></tr><tr><td>D2</td><td>-21</td><td>-38</td><td>40</td><td>-28</td><td>-25</td><td>42</td><td>-33</td><td>-33</td><td>50</td><td>-32</td><td>-26</td><td>53</td><td>2.504</td><td>.475</td><td>-45</td><td>-22</td><td>50</td></tr><tr><td>D3</td><td>-26</td><td>-41</td><td>30</td><td>-37</td><td>-27</td><td>55</td><td>-33</td><td>-33</td><td>51</td><td>-30</td><td>-29</td><td>50</td><td>1.534</td><td>.675</td><td>-43</td><td>-22</td><td>53</td></tr><tr><td>D4</td><td>-48</td><td>-31</td><td>48</td><td>-31</td><td>-26</td><td>44</td><td>-41</td><td>-30</td><td>52</td><td>-30</td><td>-26</td><td>55</td><td>2.997</td><td>.392</td><td>-40</td><td>-27</td><td>55</td></tr><tr><td>D5</td><td>-21</td><td>-54</td><td>48</td><td>-31</td><td>-27</td><td>46</td><td>-32</td><td>-34</td><td>44</td><td>-24</td><td>-29</td><td>57</td><td>5.132</td><td>.162</td><td>-40</td><td>-24</td><td>56</td></tr></tbody></table>

low frequency than high frequency. The number of electrical dipoles with good quality of fit was higher compared to all vibrotactile conditions. 10 Hz vibrotactile dipoles appeared to be the farthest from reference data recorded by fMRI, while 50-, 250 Hz vibrotactile and electrical were relatively closer in space. SEPs and sources observed in this study are consistent with previous work, particularly that which has described rapid cortical responses to finger tactile stimulation, electrical or vibrotactile (Forschack et al., 2020; Houzé et al., 2011; Jamali and Ross, 2012; Kalogianni, Daffertshofer, et al., 2018).

## 4.2. Electrical stimulation elicits earlier cortical responses compared to vibrotactile stimulation, leading to distinct connectivity patterns

The temporal dynamics of cortical responses differ significantly between electrical and vibrotactile stimulations. Electrical stimulation elicits earlier latencies, starting at P1 and extensively increasing with the N1 and P2 components, marking a quicker onset of sensory processing. Similarly, (Rossini et al., 1996) compared electrical stimulation to air puff of the finger, where electrical stimulation consistently produced faster responses. In contrast, (Kawohl et al., 2007) comparing electrical

and pressure stimulation found no significant latency differences. Vibrotactile stimulation, however, leads to more delayed responses, notably the N1 and P2 components. These latency differences suggest that electrical stimulation promotes faster initial information processing, while vibrotactile stimulation involves more temporally protracted sensory integration. This temporal shift in processing could underlie the distinct ways in which each type of stimulation is integrated into cortical networks. These differences are further supported by the observed cortical connectivity patterns, which revealed significant distinctions between the two types of stimulation, suggesting distinct mechanisms of sensory integration. The earlier connectivity observed for P1 and N1 after 250 Hz vibrotactile stimulation may indicate a more localized and focal integration process, potentially linked to the specific frequency of the stimulation, compared to electrical stimulation. In contrast, the increased widespread connectivity at P2 following electrical stimulation suggests that this type of stimulation may promote a more global integration of tactile information, possibly involving regions beyond the primary somatosensory cortex.

Our connectivity patterns revealed distinct neural integration mechanisms between electrical and vibrotactile stimulations, with
<figure><img src="image_11.png" /><figcaption>**Fig. 8. Equivalent current dipole (ECD) fitting.** A) ECD source localization dipole coordinates plotted at P1 in MNI space on glass brains. VIBR 10 Hz dipoles are located on the parietal cortex, while ELEC and VIBR 50 and 250 Hz are over the postcentral gyrus. B) ECD source localization dipole ellipses plotted in three-dimensional space. Only dipoles with a goodness of fit ≥ 70 % were included in the analysis. This resulted in retaining 55 % of the ELEC dipoles, 19 % of the VIBR 10 Hz dipoles, 24 % of the VIBR 50 Hz dipoles, and 40 % of the VIBR 250 Hz dipoles. VIBR dipoles are not following the somatotopic organization, ELEC dipoles are closer to the realistic somatotopy. Ellipsoids represent 95 % confidence interval. Stars represent reference data from (Holmes and Tamè, 2019).</figcaption></figure>

<caption>**Table 3**</caption>

**Comparison of mean MNI vectors and distances to reference.** This table presents the mean vectors (x, y, z) and their norms (distances from the reference) derived from individual MNI coordinates, which were aligned with the reference coordinates from (Holmes and Tamè, 2019). The reference coordinates were subtracted from the individual data, and the resulting values were averaged by finger and condition. An additional row aggregates these values across all fingers to represent the overall deviation from the reference for each condition. The norms quantify the distance of the mean location computed in the study from the reference coordinates. Data are depicted in millimeters (mm).

<table><thead><tr><th rowspan="2"></th><th colspan="3">VIBR 10 Hz</th><th colspan="3">VIBR 50 Hz</th><th colspan="3">VIBR 250 Hz</th><th colspan="3">ELEC</th></tr><tr><th>x</th><th>y</th><th>z</th><th>norm (mm)</th><th>x</th><th>y</th><th>z</th><th>norm (mm)</th><th>x</th><th>y</th><th>z</th><th>norm (mm)</th><th>x</th><th>y</th><th>z</th><th>norm (mm)</th></tr></thead><tbody><tr><td>D1</td><td>5</td><td>-33</td><td>-19</td><td>38.5</td><td>8</td><td>-7</td><td>-14</td><td>17.4</td><td>10</td><td>-1</td><td>-11</td><td>15.0</td><td>13</td><td>-4</td><td>-3</td><td>13.7</td></tr><tr><td>D2</td><td>22</td><td>-15</td><td>-12</td><td>29.3</td><td>14</td><td>-2</td><td>-10</td><td>17.8</td><td>10</td><td>-10</td><td>-3</td><td>14.4</td><td>11</td><td>-3</td><td>1</td><td>11.2</td></tr><tr><td>D3</td><td>17</td><td>-18</td><td>-22</td><td>33.2</td><td>6</td><td>-4</td><td>2</td><td>7.9</td><td>10</td><td>-10</td><td>-1</td><td>14.4</td><td>13</td><td>-6</td><td>-2</td><td>15.0</td></tr><tr><td>D4</td><td>-5</td><td>-9</td><td>-4</td><td>11.0</td><td>-12</td><td>-3</td><td>-8</td><td>14.9</td><td>2</td><td>-8</td><td>-1</td><td>8.2</td><td>12</td><td>-4</td><td>3</td><td>13.3</td></tr><tr><td>D5</td><td>22</td><td>-32</td><td>-4</td><td>38.9</td><td>-12</td><td>-4</td><td>-6</td><td>14.3</td><td>11</td><td>-11</td><td>-8</td><td>17.3</td><td>19</td><td>-6</td><td>4</td><td>20.5</td></tr><tr><td>Mean</td><td>12</td><td>-21</td><td>-12</td><td>27.5</td><td>10</td><td>-4</td><td>-7</td><td>13.4</td><td>9</td><td>-8</td><td>-5</td><td>12.7</td><td>14</td><td>-5</td><td>0</td><td>14.4</td></tr></tbody></table>

strong connectivity between the left paracentral lobule and the left parietal cortex for both types of stimulation, previously observed in sensorimotor integration (Huang et al., 2022). However, vibrotactile stimulation induced connections between the left orbitofrontal cortex and the left posterior cingulate cortex, which were absent in the electrical stimulation, suggesting a different sensory processing involving higher-level networks (Rolls et al., 2023b; Sasaki et al., 2024).

These connectivity differences may also reflect distinct modes of sensory processing, where electrical stimulation is more related to interhemispheric connections, while vibrotactile stimulation is more associated with specific intra-hemispheric integration, as supported by (González-Garrido et al., 2017). This results could be explained by the fact that electrical stimuli would be directly delivered to both contralateral S1 and S2 through parallel processing (Liang et al., 2011), while vibrotactile stimuli would be transmitted from contralateral S1 to S2 through serial processing (Kalberlah et al., 2013), in accordance with (Chung et al., 2014). There is also neuroimaging evidence from fMRI studies that repetitive electrical stimulation specifically enhances

connectivity in the associative areas of the sensorimotor network, as a result of a plasticity process (Heba et al., 2017). Our results suggest that vibrotactile stimulation may preferentially engage the ventral somatosensory stream, involved in multimodal integration and body representation, as evidenced by the observed fronto-cingulate and fronto-parietal connectivity (Rolls et al., 2023a). Finally, the reduced fronto-cingulate connectivity in the electrical condition may reflect a more direct and localized integration process, where electrical stimulation, by simultaneously activating all afferent fibers, could engage a more efficient and rapid pathway through inter-hemispheric connections.

### 4.3. Electrical stimulation provides more accurate cortical activation than vibrotactile stimulation and is associated with reliable finger mapping

Cortical activation differences between the electrical and vibrotactile stimulations, as well as variation in dipole localization, have not been documented. The latency differences between electrical and vibrotactile
stimulation, particularly at P1 and N1, suggest distinct neuronal pro-
cessing pathways. Electrical stimulation, which activates all afferent
fibers non-specifically, may induce a cumulative effect where the
simultaneous recruitment of multiple fiber types enhances the response
of fast-adapting fibers (FA1 and FA2). Fast-adapting fibers, such as FA1
afferents, are characterized by their response to the onset and offset of a
stimulus, which can lead to rapid temporal responses that dominate
early cortical activity. In contrast, slowly adapting fibers (SA1 and SA2)
provide sustained encoding throughout the stimulation period. This
difference in response behavior suggests that the rapid activation of FA
fibers is likely facilitated by their interaction with SA fibers, especially
during high-frequency stimulation. Furthermore, the cortical organiza-
tion of FA and SA afferents shows overlapping spatial clusters, allowing
both types to contribute simultaneously to sensory processing of a single
skin surface, thereby enhancing the efficiency and speed of sensory
encoding. These findings are supported by previous studies (Whitsel,
2001; Pei et al., 2009; Sur et al., 1984). This broad activation could lead
to greater temporal summation and synchronization, resulting in shorter
latencies in the primary somatosensory cortex. While slow-adapting fi-
bers (SA1 and SA2) are also recruited, the facilitated response of
fast-adapting fibers may dominate the early cortical activity.
Conversely, vibrotactile stimulation, independently of the vibration
frequency and thus the activated mechanoreceptors, may involve more
complex processing (e.g., temporal summation), with additional steps of
synaptic modulation, which would explain the longer latencies (Abraira
and Ginty, 2013; Ackerley and Kavounoudias, 2015; Deflorio et al.,
2022). The increased SEP amplitude at N1 and P2 for electrical stimu-
lation may reflect stronger neuronal synchronization or greater cortical
activation. These results support the idea that electrical stimulation, due
to its non-specific fiber nature, simultaneously activates a combination
of different mechanoreceptors, each one uniquely tuned and firing with
distinct patterns (Handler and Ginty, 2021; Lumpkin et al., 2010; Vallbo
and Johansson, 1984), leading to a more intense cortical response.

The analysis of dipole localization revealed that electrical stimula-
tion generates a somatotopic organization consistent with previous EEG
(Houzé et al., 2011; Kalogianni, Daffertshofer, et al., 2018) and fMRI
literature (Holmes and Tamè, 2019), with dipoles localized on the
postcentral gyrus, the cortical digit representations being arranged in an
orderly somatotopic way from lateral-to-medial and
inferior-to-posterior organizations from digit 1 to digit 5 (Baumgartner
et al., 1991; Janko et al., 2022; Khalife et al., 2022; Kikkert et al., 2023;
Kim et al., 2014; Martuzzi et al., 2014; Sanders et al., 2023; Schellekens
et al., 2021). This accuracy could be attributed to the direct and focused
activation of primary somatosensory pathways. In contrast, dipoles
resulting from vibrotactile stimulations show a broader dispersion,
particularly at 10 Hz, which could indicate a greater and earlier
participation of associative cortical regions.

4.4. *High-frequency vibrotactile stimulation yields promising results for finger mapping, but not low-frequency*

Our findings highlight distinct differences in the efficacy of vibro-
tactile stimulation at varying frequencies for finger mapping. High-
frequency stimulation at 250 Hz, which aligns with the optimal range
for activating Pacinian corpuscles (which are sensitive to high-frequency
vibrations) (Klöcker et al., 2016), demonstrated a robust response. This
frequency produced consistent and reliable cortical activations, sug-
gesting its potential for precise finger mapping. The high sensitivity of
Pacinian corpuscles to rapid vibrations likely contributes to the strong
somatotopic organization observed within the primary somatosensory
cortex during 250 Hz stimulation. Studies using fMRI and iEEG have
consistently shown that high-frequency vibrotactile stimulation (> 100
Hz) activates primary somatosensory areas for tactile detection and
accurate finger location discrimination, but also engages higher-level
processing regions like the superior parietal lobule, the inferior parie-
tal lobule, the posterior parietal cortex and the supra-marginal gyrus.

Notably, it has been found to robustly activate S1 and S2, with S2
exhibiting greater sensitivity and broader spatial integration of sensory
information (Chung et al., 2013; Kalberlah et al., 2013; Kim et al., 2014;
Ryun et al., 2023; Schellekens et al., 2021).

In contrast, lower frequencies, particularly at 10 Hz, primarily target
Merkel cells (responsive to pressure and texture), were significantly less
effective, with responses showing reduced spatial accuracy and less
reliable activation patterns. This reduced reliability may be due to the
less dynamic nature of the stimuli these receptors detect, which might
not translate as effectively into accurate cortical mapping under the
experimental conditions used. In the past, low-frequency vibrations (<
20 Hz) have only been studied using fMRI, which also failed to show
significant activation patterns in the postcentral gyrus (Cheyne et al.,
1998; Wang et al., 2013).

The 50 Hz frequency, which corresponds to the peak sensitivity of Meissner corpuscles (sensitive to low-frequency vibrations), offered intermediate results. While our results were better than 10 Hz, it still fell short of the accuracy observed at 250 Hz, possibly due to the mixed and less specific nature of the receptor activation at this frequency. Those results align with those of (Bolton and Staines, 2011), who found that EEG did not produce robust outcomes with low-frequency vibrations. Conversely, iEEG and MEG studies were more effective in reliably mapping sensory responses in S1 and S2 (Jamali and Ross, 2012; Ryun et al., 2023). However, fMRI studies have shown strong performance with low-frequency vibrotactile systems (20–60 Hz), especially in the 40–50 Hz range. These studies revealed distinct cortical activations in both primary and secondary somatosensory areas and provided a reliable differentiation of finger representations (Asghar et al., 2023; Chung et al., 2013; Kalberlah et al., 2013; O'Neill et al., 2020; Rabe et al., 2023; Schellekens et al., 2021). Consequently, low-frequency vibrations appear to be more effectively suited for fMRI studies compared to scalp electrophysiology.

Overall, these findings suggest that high-frequency vibrotactile
stimulation, by specifically targeting the rapidly adapting Pacinian
corpuscles, can be a valuable tool for detailed finger mapping. In
contrast, lower frequencies, which engage mechanoreceptors associated
with less dynamic stimuli, produce cortical activations with reduced
resolution and consistency for precise tactile processing studies. This
contrasts with electrical stimulation, which likely stimulates all types of
mechanoreceptors non-specifically and non-ecologically, leading to less
specialized but broader cortical activation.

4.5. *Limitations*

Some limitations should be considered. Firstly, the peak observed at onset (t = 0) in the SEPs following electrical stimulation appears to be a stimulation artifact. Although baseline correction was systematically applied to minimize its impact on subsequent signals (Barban et al., 2019), some residual effects may still persist. Additionally, while we controlled for stimulation parameters such as duration, location, and frequency, stimulation intensity had to be individually adjusted for each participant to avoid nociceptive activation. This adjustment might lead to small variations in amplitude. Furthermore, due to the absence of individual MRIs, we used a normalized MNI template to create our head models, which may affect the accuracy of source localization. Finally, our study involved two distinct cohorts, each with its own intra-subject variability. We ensured consistency in stimulation intensity and SEP parameters between cohorts and employed appropriate statistical tests to account for this variability. Nonetheless, cohort-specific differences should be considered when interpreting our results.

**5. Conclusion**

This study provides comparative EEG results on cortical responses,
brain connectivity and focal and distributed source localization induced
by two types of tactile finger stimulation: electrical stimulation, and
vibrotactile stimulation at different frequencies. Our results demonstrate that although both types of stimulation activate the primary somatosensory cortex and associative areas, they lead to distinct temporal and spatial processing. In contrast, different frequencies in vibrotactile did not differ between each other. Vibrotactile stimulation shows significantly delayed latencies compared to electrical stimulation, indicating a different temporal processing pathway. Spatial processing was different between both stimulations, providing distinct connectivity patterns and sources with electrical stimulation activating in S1 and SPL, while vibrotactile stimulation resulting in posterior activation across the sensory associative areas, such as IPS and SPL. Lastly, the somatotopic organization following electrical stimulation was in line with previous fMRI studies, but not high-frequency vibrotactile stimulation. The results are informative for future surface EEG studies aiming to elucidate the cortical mechanisms of finger sensory processing.

## Funding

AA received doctoral grant support from French National Research Agency (ANR-18-RHUS-0014 project "PsyCARE").

## CRediT authorship contribution statement

**Anaëlle Alouit:** Writing – review & editing, Writing – original draft, Visualization, Software, Resources, Project administration, Methodology, Investigation, Formal analysis, Data curation, Conceptualization.
**Martine Gavaret:** Writing – review & editing, Methodology. **Céline Ramdani:** Writing – review & editing, Methodology. **Påvel G. Lindberg:** Writing – review & editing, Validation, Supervision, Project administration, Investigation, Funding acquisition, Conceptualization.
**Lucile Dupin:** Writing – review & editing, Visualization, Validation, Supervision, Resources, Project administration, Methodology, Investigation, Formal analysis, Conceptualization.

## Declaration of competing interest

The authors have declared no conflict of interest.

## Acknowledgments

We acknowledge the sensorimotor platform core facility of BioMedTech Facilities INSERM US36 | CNRS UAR2009 | Université Paris Cité, and Danping Wang, Université Paris Cité, INSERM, CNRS, BioMedTech Facilities, F-75006 Paris, France, for assistance with the generation of electrophysiological data. We thank Michele Tagliabue, Université Paris Cité, INCC UMR 8002, CNRS, F-75006 Paris, France for help with participant recruitment.

## Supplementary materials

Supplementary material associated with this article can be found, in the online version, at doi:10.1016/j.neuroimage.2025.121249.

## Data availability

Data will be made available on request.

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