# Does your brain feel virtual touch as a physical touch? Influence of haptics and cognitive workload on touch perception in VR

Emile Savalle, Léa Pillette, Kyungho Won, Ferran Argelaguet, Anatole Lécuyer, Marc J.-M. Macé

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Emile Savalle, Léa Pillette, Kyungho Won, Ferran Argelaguet, Anatole Lécuyer, et al.. Does your brain feel virtual touch as a physical touch? Influence of haptics and cognitive workload on touch perception in VR. VR 2026 - IEEE Conference on Virtual Reality and 3D User Interfaces, Mar 2026, Daegu, South Korea. hal-05479932

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Submitted on 27 Jan 2026

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# Does your brain feel virtual touch as a physical touch?
## Influence of haptics and cognitive workload on touch perception in VR

Emile Savalle*

Univ. Rennes, Inria, CNRS, IRISA

Ferran Argelaguet

Inria, Univ. Rennes, CNRS, IRISA

Léa Pillette

Univ. Rennes, Inria, CNRS, IRISA

Anatole Lécuyer

Inria, Univ. Rennes, CNRS, IRISA

Kyungho Won

Incheon National University

Marc Macé

Univ. Rennes, Inria, CNRS, IRISA

<figure><img src="image_3.png" /><figcaption>Figure 1: Experimental setup used to investigate the impact of haptic feedback and cognitive load on the perception of touch in VR. (Left) Real environment: (A) Electroencephalography headset (48-electrode g.Tech EEG cap), (B) VR headset (HTC Vive), (C) 6-axis robotic arm (UR5), and (D) Motion tracking (Leap Motion system). (Right) Virtual environment replicating the real environment.</figcaption></figure>

## ABSTRACT

Immersion in virtual reality is a powerful sensory experience. It often leads to striking situations, such as experiencing virtual contacts on your avatar, despite the absence of physical contact on your real body. However, as of today, it is still unknown whether purely-virtual touch remains fictive/imaginative, or whether our brain processes it the same way as real touch. In this paper, we conducted an experiment comparing purely visual touch simulation (a virtual robot arm touching the avatar's hand) with a congruent visuo-tactile simulation (visual feedback is coupled with a real robot arm touching the participant's hand in the physical world). Using this setup, we investigated the neural responses to purely-virtual and visuo-tactile touch, and examined how these responses were modulated by cognitive workload induced by tasks within the virtual environment. The analysis of the electroencephalography (EEG recording) revealed a strong activation of the sensorimotor cortex in both conditions, although stronger and faster when physical touch occurs. Our results also revealed a decrease in evoked potentials under high cognitive workload, with the reduction being more pronounced for physical touch. Our results show that a purely-virtual touch is processed similarly to a physical touch, i.e, engaging sensorimotor regions despite the absence of direct somatosensory stimulation. This suggests that the perception of the body in virtual environments can extend beyond simple illusions, involving neural processes similar to those engaged during real physical experiences. Besides, in the age of metaverses becoming accessible to a wider audience, know-

ing that our brain perceives virtual contacts in VR similarly to physical contacts further underlines the need to design immersive environments that prioritize physical integrity and user safety.

**Index Terms:** BCI, VR, Haptic, Sensorimotor, EEG, Robotic

## 1 INTRODUCTION

Virtual reality can generate powerful illusions of touch, with users reporting strong tactile sensations, despite the absence of a physical counterpart [27]. This suggests that visual cues, alone, are able to trigger sensations usually associated with haptic stimulation [33]. These illusions are often attributed to visual dominance, in the context of multisensory integration, as exemplified by the famous rubber hand illusion [41] - where the brain prioritizes visual information over other sensory modalities.

However, it remains unclear whether the sensations induced by purely visual touch remain imaginative, or if they match with those of real touch and physical stimulation, i.e., with a similar "somatosensory processing". In other words, does our brain process purely-visual touch in VR similarly to a physical touch?

Electroencephalography (EEG) provides a direct way to study the neural responses to sensory stimulation. For example, when a tactile stimulation occurs, a specific activity is produced in the sensorimotor cortex [30]. These activation patterns are also referred to as "neural markers" and "event-related potentials" [44]. They can be used to assess how the user processes a perceived stimulation, whether it is haptic, visual, or both. The presence or absence of such markers can thus inform whether a given experience is inducing genuine somatosensory processing.

Furthermore, it is important to consider that many tasks performed in VR involve manual interaction, such as grabbing, assembling, or manipulating virtual objects. These interactions not only rely on visual and tactile cues but also demand cognitive re-

<footnote>*e-mail: emile.savalle@gmail.com</footnote>
sources to follow instructions, memorize sequences, or make decisions. As a result, such tasks can generate significant cognitive workload, which can interfere with the way sensory events are processed, as cognitive workload is known to impact perception and the associated neural responses [46]. For example, an increased cognitive load has been associated with a reduced neural response to tactile stimuli [29].

In this paper, we conducted an experiment to compare the neural responses to a purely-visual touch sensation, i.e., in the absence of physical contact in the real world, with that of a congruent visuo-tactile sensation of contact, i.e., in the presence of physical touch. Participants were exposed to either visual-only feedback (a virtual robot arm touching the avatar's hand) or visuo-tactile feedback (visual feedback coupled with a real robot arm touching the participant's hand). Additionally, we used a classical n-back task to modulate the user's cognitive load and assess the impact of this cognitive load on neural responses evoked in the different conditions.

Our study reveals that a purely visual-touch stimulation evokes a strong activity in the sensorimotor cortex, despite the absence of physical contact, in a similar manner (in terms of event-related potentials and de/synchronisation of neural processes) as a physical touch. We also found that experiencing the physical stimulation before the purely-visual one increases the brain reaction to visual touch. Last, we found that cognitive workload strongly decreases the brain reaction to physical touch, but has a lesser effect on the purely-visual condition.

# 2 RELATED WORK

## 2.1 Perception of Touch in VR

Touch sensations can be delivered using tactile or kinesthetic feedback devices [42]. Yet, haptic interfaces are often bulky, expensive, or highly specialized, limiting their applicability to specific use cases and reducing their integration in general VR systems [42]. To overcome these limitations, researchers have explored virtual haptic illusions, which aim to evoke the sensation of touch through visual, auditory, or contextual cues without requiring physical stimulation [25]. These haptic illusions first rely on illusions of ownership over fake or virtual limbs [39].

The most well-known example of such an illusion is the rubber hand illusion, where synchronized visual and tactile stimuli lead individuals to experience ownership and tactile sensation over a fake limb [2]. These types of illusions have been replicated in virtual reality, namely through the “virtual arm illusion” [38], where synchronous visual and tactile stimulation of both physical and virtual limb can create a strong feeling of ownership over the virtual body. These illusions of ownership are typically rooted in multisensory integration, where the brain combines visual, tactile, and proprioceptive cues to construct a coherent sense of body ownership [39]. Additionally, a key factor to produce these illusions is the avatar, having a virtual body, particularly one that is congruent with the user's body, responsive, and physically matching the user, has been shown to significantly improve the strength and realism of these illusions [36].

Once ownership over the virtual body is established, users may begin to experience vivid tactile sensations in response to events occurring only within the virtual environment [33]. These sensations, often referred to as “phantom” or “illusory” touches, are triggered by visual or contextual cues, such as virtual objects touching the avatar's skin, even in the complete absence of physical contact. This phenomenon suggests that under strong virtual ownership conditions, the brain can attribute sensory experiences to the virtual body, filling in the missing tactile input based on sensory expectation and multisensory coherence [33].

However, it remains unknown if the brain processes the perception of a virtual touch in a similar way as it would a physical one.

## 2.2 Perception of Touch Through EEG

Precisely quantifying touch sensations is a complex endeavor, and typically assessed through questionnaires or behavioural responses [16, 19, 8, 40]. To deepen the knowledge gained from these metrics, EEG can be used to study touch sensations, as it allows the evaluation of neural responses to the touch stimulus in real-time.

Neurohaptics refers to the intersection of haptic technology and neuroscience [1], where touch is either the subject of study or a tool for investigating brain processes. Using haptic stimulation, several works have focused on the assessment of neural responses using EEG, MEG, or fMRI in order to study the impact of pressure [37], vibration [11], or texture [47], and to investigate the neural mechanisms involved in processing these stimuli.

Furthermore, neurohaptic setups have been used to examine how cognitive workload modulates tactile perception, showing that increased mental demand can alter sensitivity to, or interpretation of, haptic cues [29].

In order to characterize the neural response to a tactile stimulation, researchers typically focus on three aspects. The first one is on the location of the activity produced in response to the stimulation. The sensorimotor band is commonly studied in somatosensory and motor research, as different body parts are represented in distinct regions of the sensorimotor cortex. For example, tactile stimulation of the left hand typically induces ERD over the right sensorimotor area (often observed around electrode CP4 in the 10-20 EEG reference system). The second aspect is the evoked patterns, and more precisely the profile of somatosensory ERPs or steady-state somatosensory-evoked potentials [10, 31]. They are specific time-locked voltage changes in the EEG signal that reflect the neural responses to specific sensory, cognitive, or motor events. Steady-state somatosensory evoked potentials (SEPs) are phase-locked EEG responses elicited by periodic tactile stimulation that reveal early cortical processing of touch, typically seen as distinct waveform components such as P50, N80, P100, and N140 following mechanical stimuli to the skin [15]. In addition to these early components, the P300 ERP has also been observed in tactile paradigms, reflecting higher-order cognitive processing of somatosensory stimuli, such as attention allocation or stimulus discrimination [45]. Unlike the early SEPs, which mainly index primary sensory processing in the somatosensory cortex, the P300 emerges later, around 300 ms after stimulus onset, or even later (450 to 600ms) [9], and is often modulated by task relevance or expectancy of tactile events. The third aspect is event-related spectral perturbations (ERSPs), which are used to analyze changes in the brain's oscillatory activity across time and frequency following a stimulus. In tactile perception, both event-related desynchronization (ERD) and synchronization (ERS) are classically observed in the sensorimotor band, in particular ERD in Alpha (8-13Hz) and Beta (13-30Hz) range [24, 44], and ERS for Delta and Theta frequencies [24].

The integration of neurohaptics with virtual reality is a relatively recent development, with only a limited number of studies exploring its potential. This emerging field combines the immersive capabilities of VR with the neural insights gained from haptic stimulation to better understand perception, cognition, and motor control. For instance, recent studies investigated tactile perception in a virtual environment. Lehser et al. investigated contactless mid-air haptics paired with a virtual brush and showed that visuo-tactile spatial mismatches elicited robust EEG markers, namely an early P120 and a later N370 resembling incongruency-related responses, alongside significantly delayed reaction times [26]. Weiss et al. examined haptic stiffness illusions with physical force feedback by manipulating the control/display ratio between real and virtual hand movements [43]. They found reliable differences in neural oscillations in frontal and parietal regions between detected and undetected illusions, with EEG-based classifiers partially distinguishing these states. Other studies have explored multisensory processing in
VR using non-invasive brain stimulation, such as transcranial direct current stimulation (tDCS), often combined with visual cues to simulate tactile sensations [48]. Furthermore, haptic feedback has been used to enhance motor imagery-based brain-computer interfaces, improving classification accuracy and user engagement within immersive environments [5]. To our knowledge, however, few studies using EEG have directly examined the perception of touch itself in VR, or how haptic feedback modulates this fundamental aspect of the virtual experience. While related work has investigated neural correlates of multisensory processing with brain stimulation, the direct measurement of somatosensory responses to virtual touch remains largely unexplored.

# 3 EXPERIMENTAL METHODOLOGY

While prior research has explored haptic technologies in VR and their neural correlates, most of these studies have focused on physical stimulation or multimodal integration. To our knowledge, no previous study has directly compared the neural responses to a purely visual touch and to a corresponding physical touch in VR. Moreover, our work is the first to investigate how cognitive workload modulates the perception of touch in immersive VR.

Thus, our study investigates the following questions:

* **RQ1:** Does a purely-visual contact elicit the same brain processes as a physical one ?

* **RQ2:** How does cognitive workload influence the perception of virtual touch ?

Our study addresses these questions by investigating whether observing a virtual touch on an embodied avatar, without any accompanying physical stimulation, can evoke neural activity patterns comparable to those elicited in the presence of a real/physical tactile input. Using EEG, we compare the neural responses to two types of stimulation: (1) a visual-only touch, where users see their virtual hand being touched, and (2) a physical touch, where the same visual stimulus is accompanied by a physical (tactile) sensation delivered by a robotic arm. We also explore how cognitive workload alters the neural responses to each type of stimulation. By introducing an n-back task of varying difficulty levels during the stimulation, we examine whether mental load influences visual and physical touch perception in similar or distinct ways.

## 3.1 Independent variables

In this experiment, we assessed two independent variables to evaluate the perception of touch in virtual reality: *haptic feedback* and *workload*. For the *haptic feedback*, we delivered visual-only and visuo-haptic stimulation to the participant's left hand to evaluate the neural response to a purely visual touch and a haptic one. For the *workload*, we applied the same stimulation to the participant's hand while participants performed a cognitive task, i.e., an n-back task, at two different levels of difficulty, thus producing a low and high level of workload. In every condition, participants were embodied in a virtual avatar matching their skin color and gender.

## 3.2 Participants and apparatus

Thirty-two participants (10 women, 22 men) took part in the experiment. We had the following inclusion criteria: only right-handed participants, and with no history of mental health issues, as left-hand stimulation affects neural responses differently in left- and right-handed individuals, and mental health conditions can alter baseline EEG activity. All participants had normal or corrected-to-normal vision. Ages ranged from 24 to 40 years, with most participants between 25 and 35 years old. Participants were then equipped with a 48-electrode g.Tec EEG cap and an HTC Vive Pro VR headset. EEG data were recorded using a g.Tec EEG cap, g.USBAmp EEG amplifiers, and the g.GAMMASYS system (g.Tec, Austria).

<figure><img src="image_4.png" /><figcaption>Figure 2: Representation of the experimental protocol depicting a first phase investigating the influence of haptic feedback on the perception of touch, and the second on the impact of cognitive workload.</figcaption></figure>

Forty-six active electrodes were placed according to the international 10–20 system (at locations Fp1, Fp2, Fz, F3, F1, Fz, F2, F4, F6, FC5, FC3, FC1, FCz, FC2, FC4, FC6, T7, C5, C3, C1, Cz, C2, C4, C6, T8, CP5, CP3, CP1, CPz, CP2, CP4, CP6, P5, P3, P1, Pz, P2, P4, P6, PO3, POz, PO4, O1, Oz, O2).

All electrodes were filled with non-abrasive electrolyte gel. EEG signals were referenced to an ear clip and grounded to AFz electrode. The data was recorded at 500 Hz using OpenViBE [34].

### 3.2.1 Virtual environment

Participants were immersed in a virtual environment developed in Unity, consisting of a neutral room with a black table and a virtual robot. The real setup matched the virtual one, with the table and the robot located at the same positions. Participants were embodied in a gender-matched avatar for which they could control the head and left-hand movements, and the rest of the body was animated using inverse kinematics, allowing them to perceive the virtual body as their own. The avatar were made using Character Creator v3, and we used Final IK for the animation. Hand movements were tracked using a Leap Motion and transmitted in real-time and synchronously to the avatar's hand. This setup ensured that visual and tactile stimuli were experienced in a first-person perspective, reinforcing the sense of body ownership [39, 20].

## 3.3 Experimental Task

Participants were requested to place their virtual hand on a precise spot over the table (see Figure 1), which ensured that the visual and tactile stimulation would be delivered consistently. The experimental task consisted of a series of 50 stimuli in each condition, in which the virtual robot virtually and repeatedly touched the users' avatar. The robot's movement was recorded and replicated on a virtual model of the robot within the VR environment in synchronicity with the real robot to produce the virtual visual touch.

In the (VISUOHAPTICCONTACT) condition, in addition to the visual stimulation, tactile stimulation was provided to the participants' left hand using a 6-axis robotic arm (Universal Robot UR5) equipped with a soft foam ball (roughly 7 cm in diameter) attached to its end-effector. The robotic arm began each trial from a starting position approximately 30 cm above the participant's hand, descending at a constant speed to make contact over a period of 1.5 seconds. Upon contact, the foam ball exerted tactile pressure for 800 ms. The pressure increased gradually over 300 ms, was maintained at a consistent level for 200 ms, and then released over the following 300 ms. After the stimulation, the robotic arm returned
<figure><img src="image_5.png" /><figcaption>Figure 3: First-person view of the virtual environment. (Left) Virtual touch (the robot arm comes into contact with the avatar's hand). (Right) Virtual touch with n-back task (a number to remember is temporarily displayed on the virtual hand).</figcaption></figure>

to its initial position. The virtual and real robot position was synchronized to ensure that the stimulation was congruent. The robotic arm operated at a very low noise level and did not produce audible cues during the task; any residual mechanical noise was masked by the VR headset's integrated headphones worn by all participants. Thus, this first phase consisted of 2 conditions, each consisting of 50 stimuli.

In addition, to modulate users' workload, we implemented an N-back task paradigm. The N-back task is a continuous performance task commonly used to manipulate and assess working memory load, in which participants must monitor a sequence of stimuli and respond when the current stimulus matches one presented N steps earlier [21]. In our implementation, whenever the robot (virtual or physical) touched the participant's hand, a new number appeared on the back of the avatar's hand at the stimulation site. Participants were instructed to press the controller trigger if the number matched either the one presented immediately before (1-back, low workload) or the one shown three steps earlier (3-back, high workload), depending on the experimental condition, chosen to create clearly distinct cognitive load levels, as intermediate (2-back) performance can be more variable and blur contrasts [18]. Participants therefore experienced conditions in this second phase, each consisting of 50 stimuli. As the number was displayed at the point of contact, it was occluded during stimulation. Participants could therefore only respond to the new number after the stimulation had ended, minimizing potential confounds.

## 3.4 Experimental Protocol

The experimental protocol is depicted in Figure 2. All participants first received detailed instructions about the study and the experimental procedure. They were informed of their rights during and after participation, and each signed an informed consent before the start of the experiment. The protocol was approved by the institutional ethical committee (n°2024-51). After the briefing, participants were fitted with the EEG cap and the VR headset before starting the experiment. Participants were asked for the avatar they preferred to embody and were asked to perform left-hand movements to ensure correct tracking and instill a sense of embodiment over the avatar (visuo-motor congruence). The experiment was divided into two distinct phases: a first phase focusing only on the impact of haptic feedback, and a second phase exploring the potential modulation of the workload.

### 3.4.1 Phase 1 - Influence of haptic feedback

The first phase of the experiment was designed to study the impact of haptic feedback on the perception of touch. To that extent, participants experienced both types of stimulation (VISUALTOUCH and

VISUOHAPTICTOUCH), and had only to look at the virtual hand. They were exposed to a series of 50 stimuli for each condition, with the order counterbalanced across participants. After each series, they completed the Avatar Embodiment Questionnaire [12] to evaluate their sense of embodiment. Each condition lasted approximately 5 minutes, resulting in a total duration of about 15 minutes (including questionnaires) for this first phase. Participants were offered a break before moving on to Phase 2.

### 3.4.2 Phase 2 - Influence of workload

In the second phase of the experiment, we investigated the impact of cognitive workload (with an N-back task) on the perception of touch. We displayed a number on top of the participant's hand while they received the visual and visuo-haptic stimulation (see Figure 3, right). The overlaid number was used to keep the participants' attention toward the virtual hand and the touch. Participants performed the n-back task consisting of 50 numbers (one for each visuo/tactile stimulation), for each level of difficulty (1-back: low workload, and 3-back: high workload) and both stimulation (VISUALTOUCH and VISUOHAPTICTOUCH). The order of stimulation types was counterbalanced, while the order of difficulty was not. Participants first performed the low difficulty level to account for the effect of fatigue. After each series, participants completed the NASA-TLX questionnaire to evaluate the impact of the task on workload. Each condition lasted approximately 5 minutes, resulting in a total duration of about 30 minutes (including questionnaires) for this second phase.

Overall, the entire experimental session lasted approximately 1 hour and 30 minutes.

## 3.5 Dependent Variables

### 3.5.1 Questionnaires

In the first part of the experiment, we assessed participants' self-reported sense of embodiment using the Embodiment Questionnaire [12]. We compared embodiment levels between VISUAL-TOUCH and VISUOHAPTICTOUCH stimulation using a repeated-measures ANOVA. In the second part of the experiment, we evaluated the impact of the n-back task on perceived workload using the NASA-TLX questionnaire [17] for both the 1-back and 3-back tasks, and for both VISUALTOUCH and VISUOHAPTIC-TOUCH stimulation. A two-factor repeated-measures ANOVA was conducted.

### 3.5.2 Event-related potentials (ERP)

Grand-average responses to touch stimulation were computed by averaging all EEG data from -500 ms to 1500 ms post-stimulation for each participant. Event-related potentials were quantified by identifying maximal amplitudes within predefined time windows (450-750 ms post-stimulus onset), following standard ERP measurement practices [32]. Statistical analyses were conducted using repeated-measures ANOVA.

### 3.5.3 Event-related de/synchronisations (ERDS)

To examine changes in frequency power, we computed event-related de/synchronisations (ERDS). Time-frequency representations were obtained between 1 and 30 Hz using Morlet wavelets. Data were normalized relative to baseline using a log-ratio of power and averaged over frequency bands. ERDS values were extracted by averaging over the stimulation window (0 to 0.8 s), and statistical analyses were performed using repeated-measures ANOVA.

### 3.5.4 Normality test

Normality of dependent variables was assessed for each experimental condition using the Shapiro-Wilk test. All groups were found to be approximately normally distributed ($p > 0.05$), supporting the use of parametric tests such as repeated-measures ANOVA.
### 3.5.5 Hypotheses

Based on our two research questions (RQ1 & RQ2), we have formulated two hypotheses which will be addressed by our experimental design:

* **H1 (touch similarity):** Visual-only and visuo-haptic stimulation of touch will elicit similar neural responses. We expect both to produce strong activation of the right sensorimotor cortex (electrode CP4), as well as comparable patterns in event-related potentials (ERPs) and event-related desynchronization/synchronization (ERD/S).

* **H2 (workload modulation):** We expect that higher cognitive workload will attenuate the neural response to touch, since workload is generally associated with a reduction in task-unrelated evoked potentials [29]. Specifically, we expect a reduction of ERP amplitudes and ERD/S magnitude under the 3-back condition compared to the 1-back condition, for both virtual and physical touch.

## 3.6 EEG Preprocessing

EEG data were preprocessed and analysed using MNE-Python [13]. A notch filter at 50 Hz was applied to reduce electrical noise, and a band-pass filter between 1 and 30 Hz was used to retain only relevant EEG frequencies. We applied an independent component analysis [6] to mitigate artifacts. We identified and removed noise-related components through visual inspection, based on their spectral, temporal, and spatial characteristics. An average of 3 components per participant were removed.

EEG epochs were time-locked to the stimulation onset ($t = 0$), and segmented from 2 seconds before to 2 seconds after onset. Baseline correction was performed using the time window from -2000 ms to -1500 ms relative to stimulation onset, period of rest preceding the robot's movements. Epochs with amplitudes exceeding $\pm 100\mu V$ were rejected as noisy. As a result, six participants were excluded due to excessive artifacts (more than 50% of epochs rejected).

# 4 RESULTS

## 4.1 Influence of haptic feedback

### 4.1.1 Subjective ratings

First, we analyzed Phase 1 of the experiment (Section 3.4.1) in which participants experienced both types of stimulation. We compared the self-reported embodiment questionnaire scores between the VISUOHAPTICTOUCH and VISUALTOUCH conditions. Participant reported an overall higher sense of embodiment with the VISUOHAPTICTOUCH stimulation ($M = 4.02, SD = 0.69$) compared to the VISUALTOUCH stimulation ($M = 2.66, SD = 1.01$), which were found to be significant using a repeated-measures ANOVA ($F(1,25) = 49.523, p < 0.001, \eta^2 = 0.33$).

### 4.1.2 EEG analysis

EEG data collected during Phase 1 (Section 3.4.1) were then analyzed to evaluate neural responses to the pure VISUALTOUCH or VISUOHAPTICTOUCH.

**Topography** Grand-average topographic maps were computed over the sensorimotor cortex for both stimulation types and displayed in Figure 4. Visual inspection revealed clear positive deflection in central electrodes (notably CP4) for both conditions. However, activation onset appeared approximately 100 ms earlier in the VISUOHAPTICTOUCH condition compared to VISUALTOUCH.

**Event-related potentials (ERP)** We then examined the grand-average event-related potentials (ERPs) over the right sensorimotor cortex (electrode CP4). As shown in Figure 5, the data reveal a strong positive potential following stimulation (peak amplitude of $5.29 \mu V$ at 550 ms for VISUOHAPTICTOUCH and $2.59 \mu V$ at 600 ms for VISUALTOUCH).

**Event-related de/synchronization (ERD/S)** We studied the changes in band power across several frequency bands. Figure 7 shows these power changes relative to baseline for Delta (1-4 Hz), Theta (4-8 Hz), Alpha (8-12 Hz), and Beta (12-20 Hz) bands, alongside ERDS values (computed as described in Section 3.5.3). Results showed Delta synchronization in response to both stimulation, with a stronger effect in VISUOHAPTICTOUCH (mean = 0.095 dB, SD = 0.127) than in VISUALTOUCH (mean = 0.056 dB, SD = 0.102). Alpha and Beta bands showed clear desynchronization during stimulation, and even though the Beta desynchronization is greater in the VISUOHAPTICTOUCH (mean = -0.114, SD = 0.157) than in the VISUALTOUCH (mean = -0.073, SD = 0.107). We computed repeated-measure ANOVAs to observe the impact of the type of stimulation on ERDS, finding a significant impact of workload on Delta frequencies ($F(1,25) = 6.59, p = .0439, \eta^2 = 0.054$). No other differences have been found significant. All p-values have been corrected for multiple tests using the Benjamini-Hochberg False-Discovery Rate (FDR) procedure.

## 4.2 Influence of workload

Finally, we analyzed data from the Phase 2 of the experiment (Section 3.4.2).

### 4.2.1 Questionnaire

We first analyzed responses to the NASA-TLX questionnaire, completed after each stimulation series during the n-back task. A two-way repeated-measures ANOVA was conducted to assess the effects of stimulation type and task difficulty on the perceived workload.

Participants reported higher workload levels during the 3-back task (visuo-haptic: $M = 3.54, SD = 0.59$; visual: $M = 3.55, SD = 0.54$) compared to the 1-back task (visuo-haptic: $M = 2.19, SD = 0.83$; visual: $M = 2.27, SD = 0.89$).

The analysis revealed a significant and strong main effect of task difficulty ($F(1,25) = 150.87, p < .001, \eta^2 = 0.474$). However, the type of stimulation had no significant effect ($F(1,25) = 0.001, p = .971, \eta^2 < 0.001$), and the interaction between difficulty and stimulation type was also non-significant ($F(1,25) = 0.35, p = .560, \eta^2 = 0.001$).

### 4.2.2 EEG analysis

**ERP** To observe the impact of workload, we performed a two-way repeated-measures ANOVA on the stimulation type and workload condition, that revealed the significant impact of workload on the positive ERP, measured between 450 ms and 750 ms post stimulus onset ($F(1,25) = 10.98, p = .014, \eta^2 = 0.02$), but no interaction between workload level and stimulation types ($F(1,25) = 4.55, p = .086, \eta^2 = 0.013$). Post-hoc analysis on the stimulation types revealed a strong reduction in the positive ERP for the VISUOHAPTICTOUCH stimulation under high workload, as shown in Figure 6. A repeated-measures ANOVA indicated a significant difference between the high workload condition ($4.04\mu V \pm 1.74$) and the low workload condition ($5.04\mu V \pm 2.31$) for this stimulation type ($p < .001$). No such difference was observed for the VISUALTOUCH stimulation.

**ERSP** Analysis of the ERSP revealed no differences for Theta, Alpha or Beta bands. However, in Delta frequencies, we observed a significant impact of the workload level ($F(1,25) = 6.59, p = .044, \eta^2 = 0.05$). FDR-corrected post-hoc analysis showed a significant
<figure><img src="image_6.png" /><figcaption>Figure 4: Grand-averaged topography elicited by haptic and visuo-haptic stimulation (Phase 1, no n-back task), showing strong right sensorimotor cortex activation for the VISUOHAPTICTOUCH condition and weaker, delayed activation for the VISUALTOUCH condition</figcaption></figure>

<figure><img src="image_7.png" /><figcaption>Figure 5: Grand-averaged event-related potentials in response to the haptic and visuo-haptic stimulation, averaged over CP4. The dotted vertical line indicates the stimulus onset and offset.</figcaption></figure>

<figure><img src="image_8.png" /><figcaption>Figure 6: Grand-average event-related potentials in response to both kinds of stimulation for both levels of workload for electrode CP4. The dotted vertical line indicates the stimulus onset and offset.</figcaption></figure>

impact of workload on the VISUALTOUCH stimulation (p = 0.043) and a close to significant one for the VISUOHAPTICTOUCH (p = 0.078).

## 4.3 Key findings

Analysis of the EEG data first revealed similar overall neural response patterns for the VISUALTOUCH and VISUOHAPTICTOUCH conditions. Averaged responses to stimulation showed strong activation of the sensorimotor cortex for both types of touch, with comparable waveforms, although responses were consistently stronger for the VISUOHAPTICTOUCH condition. We further examined the impact of cognitive workload on touch perception. EEG analyses revealed a reduction in neural responses to touch under higher workload conditions. This attenuation was observed for both stimulation types but was more pronounced for the VISUOHAPTICTOUCH condition.

# 5 DISCUSSION

In this study, we designed an experiment to measure the perception of touch in virtual reality using EEG. We first compared neural responses evoked by a virtual touch, delivered visually in VR, to those evoked by a physical touch, provided by a robotic arm combined with the same visual stimulation. Our design aimed to observe the similarities and differences between these two types of touch.

We started by addressing our first hypothesis: **HI: Visual-only and visuo-haptic stimulation of touch elicit similar neural responses.** EEG data analysis from Phase 1 revealed that both types of stimulation activated parietal regions, particularly over the

right sensorimotor cortex, which we found to be coherent with the literature, as hand sensations are localized contralaterally around CP3/CP4 [31].

For the VISUOHAPTICTOUCH condition, this is coherent with the literature [31] as the processing of hand sensation is located over the sensorimotor cortex (CP4 for left hand, CP3 for right hand). However, this study shows for the first time that the visual-only hand stimulation of an embodied avatar elicits an activation of the sensorimotor cortex, showing a similar pattern as in the visuo-haptic condition. The later positive peaks we observe, compared with classical somatosensory evoked potentials (SEPs) that typically arise within 100-300ms, are consistent with literature showing that tactile and visuotactile P300 or LPP components often occur around 450-550ms and can extend beyond 600ms, with timing influenced by task complexity and attentional demands [9].

In addition, visuotactile paradigms involving feedback, embodiment, or multisensory congruence have been shown to elicit sustained late positivities (LP/LPP) starting around 400-500ms and lasting up to 800-1200ms [14, 4]. These late components are generally thought to reflect higher-level processing, such as evaluating the relevance of the touch to the body and integrating information across different senses.

The topographic maps show a slower response for the VISUAL-TOUCH. However, there is an ambiguity as to whether this delay reflects slower neural processing of the touch itself, or whether it results from the virtual robot obscuring the exact moment of contact, leading to a delayed reaction. Moreover, we observed a similar ERP pattern for both stimulation types, although the response was
<figure><img src="image_9.png" /><figcaption>Figure 7: Grand-averaged Event-Related De/Synchronisation for the frequency bands: (a) Delta 1-4Hz (b) Theta 4-8Hz (c) Alpha 8-12Hz (d) Beta 12-20Hz. Top: Power frequency change over time, with SEM. Vertical dotted lines represent stimulus onset and offset. Bottom: Representation of computed ERS/D over the stimulation window.</figcaption></figure>

<figure><img src="image_10.png" /><figcaption>Figure 8: Grand-averaged Event-Related De/Synchronisation for each stimulation and workload level. Vertical dotted lines represent start and stop of touch stimulations. * indicates the significant impact of workload on the average ERSP in the stimulation window.</figcaption></figure>

faster and stronger in the physical condition. Finally, we observed similar changes in band power across theta, alpha, and beta frequencies for both stimulation types. In particular, both conditions elicited alpha and beta ERD in the somatosensory cortex, consistent with tactile processing [3, 28]. However, only Delta frequency synchronization was significantly stronger in the VISUOHAPTIC-TOUCH condition. Conversely, the observed difference in the Delta frequency band (stronger ERS for the haptic stimulation) could be explained by its greater salience, as salience has been shown to impact tactile responses [7, 22]. These results partly confirm H1, as they suggest that an embodied virtual touch, even if purely visual, can elicit neural activity similar to that evoked by physical touch, although with notable differences in latency and amplitude.

During the first phase of the experiment, we also aimed at assessing the impact of the stimulation condition on the perceived sense of embodiment. Participants reported a higher sense of embodiment when the visual stimulation was accompanied by physical touch, consistent with previous findings in the haptic literature, as the congruent combination of multiple modalities can elicit a stronger sense of embodiment [35].

The second part of the experiment aimed to assess the impact of cognitive workload on the neural response to touch. In this respect, we hypothesized that higher cognitive workload will attenuate the neural response to touch (H2). To that end, with data collected in Phase 2, we compared the neural responses across low and high workload conditions for both types of stimulation (visual-only, visuo-tactile). First, the NASA-TLX questionnaire confirmed the effectiveness of our workload manipulation: partici-

pants reported significantly higher perceived workload in the high difficulty condition, with no difference between stimulation types. Through the EEG analysis, we could observe a significant reduction in event-related potentials during the VISUOHAPTIC-TOUCH condition under high workload conditions, although this was not observed in the VISUAL-TOUCH condition. However, in the event-related de/synchronizations, we observed an impact of the workload condition on both stimulation conditions on Delta frequencies. Although not significant, the observed decrease in alpha power is consistent with the literature, as alpha desynchronization amplitude has been observed to decrease under high cognitive load [29].

These results validate our second hypothesis H2, by showing that cognitive workload reduces sensorimotor processing during physical stimulation, in line with previous literature reports [29]. However, only a small impact was observed for visual-only stimulation. We hypothesize that the evoked responses in the visual condition, because they were weaker, made it more difficult to detect an effect of the workload.

# 6 LIMITATIONS AND FUTURE WORK

One limitation of our study is that the physical stimulation was not individually calibrated. To preserve the novelty of the touch, we did not individually calibrate the touch, thus applying identical physical touch for all participants. Consequently, differences in hand size led to small variations in the timing and duration of contact, which may have introduced variability in the neural responses, potentially inducing variability in evoked potentials latency. However, the impact of this variability on the results is likely limited, as the
sample size (N = 32) and the repeated-measures design reduce the influence of individual differences. Small variations in contact timing would have been averaged out at the group level, minimizing their effect on the overall ERP and ERDS analyses. Furthermore, although habituation to tactile stimulation may have been present, it is unlikely to have significantly affected the results, as individual stimuli were separated by more than one second and were sufficiently spaced to prevent strong adaptation effects.

A second limitation concerns the embodiment of participants. While embodiment was induced by displaying and moving a virtual hand, no dedicated procedure was implemented to strengthen body ownership (e.g., through synchronous visuo-motor calibration). The lack of embodiment elicitation phase could explain the mitigated embodiment scores, which could have influenced the extent to which participants experienced the VISUALTOUCH as being directed to their own body. However, from the results, the procedure was still enough to elicit a sensation of visual touch.

Future investigations should examine whether a stronger sense of embodiment modulates the neural response to virtual touch, as higher body ownership might enhance the illusion and bring neural processing closer to that of real touch. Another worthy direction would be to extend the current paradigm to the domain of social touch in VR, where having a virtual character providing the touch could influence the perception of the tactile stimulation.

Our study also focused exclusively on passive touch, as this was simpler to control experimentally. However, active touch, where participants initiate the movement, would likely engage motor processes, producing different neural dynamics. Comparing passive and active paradigms would therefore provide valuable insight into the full range of touch perception in VR.

Future work could explore combining physical touch with augmented or mixed-reality overlays, where virtual cues enhance the perception of contact or embodiment. Such integration might modulate neural responses and offer richer multisensory interactions in immersive environments.

Finally, the robotic touch we used was deliberately neutral, lacking emotional or social meaning. Yet touch is often inherently affective, and previous research suggests that emotional or socially meaningful touches (e.g., comfort, intimacy, or threat) influence the neural responses [23]. Expanding the paradigm to include affective VR touches would thus be an important next step.

## 7 CONCLUSION

In this paper, we investigated touch perception in Virtual Reality, aiming to characterize the cognitive processes behind purely-visual touch and how it compares to a physical touch. Using a 6-axis robotic arm to deliver haptic stimulation, we compared an embodied visual touch with and without concurrent physical feedback. Our findings demonstrate that a virtual and purely-visual touch can still elicit neural activity in parietal regions, particularly over the sensorimotor cortex, corresponding to the stimulated limb, even in the absence of physical contact. We also found that a higher cognitive workload significantly dampens the neural response to physical touch in VR. Overall, our results show that the brain processes the two touch stimulations in a similar manner, highlighting the importance of considering tactile perception in the design of future immersive environments.

## ACKNOWLEDGMENTS

The authors wish to thank A, B, and C. This work was supported in part by a grant from XYZ.

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