The Effects of Subthreshold Vibratory Noise on Cortical Activity During Motor Imagery

in Motor Control

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Kishor LakshminarayananDepartment of Sensors and Biomedical Engineering, School of Electronics Engineering, Vellore Institute of Technology, Vellore, India

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Rakshit ShahCleveland State University, Cleveland, OH, USA

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Yifei YaoShanghai Jiao Tong University, Shanghai, SHG, China

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Deepa MadathilJindal Institute of Behavioural Sciences, O. P. Jindal Global University, Haryana, India

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DOI:
https://doi.org/10.1123/mc.2022-0061
Keywords:
virtual reality; vibratory stimulation; kinesthetic motor imagery; brain–computer interface; electroencephalography; event-related desynchronization; machine learning
First Published Online:
17 Feb 2023
In Print:
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Page Range:
1–14
Free access

Previous studies have demonstrated that both visual and proprioceptive feedback play vital roles in mental practice of movements. Tactile sensation has been shown to improve with peripheral sensory stimulation via imperceptible vibratory noise by stimulating the sensorimotor cortex. With both proprioception and tactile sensation sharing the same population of posterior parietal neurons encoding within high-level spatial representations, the effect of imperceptible vibratory noise on motor imagery-based brain–computer interface is unknown. The objective of this study was to investigate the effects of this sensory stimulation via imperceptible vibratory noise applied to the index fingertip in improving motor imagery–based brain–computer interface performance. Fifteen healthy adults (nine males and six females) were studied. Each subject performed three motor imagery tasks, namely drinking, grabbing, and flexion–extension of the wrist, with and without sensory stimulation while being presented a rich immersive visual scenario through a virtual reality headset. Results showed that vibratory noise increased event-related desynchronization during motor imagery compared with no vibration. Furthermore, the task classification percentage was higher with vibration when the tasks were discriminated using a machine learning algorithm. In conclusion, subthreshold random frequency vibration affected motor imagery–related event-related desynchronization and improved task classification performance.

Motor imagery (MI) improves motor performance in subjects through strengthening motor task–related neuronal signaling via mental “rehearsal” of defined movement sequences (Schuster et al., 2011). Similar MI techniques have been integrated into rehabilitation strategies for patients who have suffered spinal cord injury (Grangeon et al., 2012) and stroke (Ietswaart et al., 2011; Malouin et al., 2013). When integrated into motor imagery–based rehabilitation protocols, virtual reality (VR) environments have been shown to improve motor performance and induce cortical plasticity (Cameirao et al., 2012; Turolla et al., 2013). VR headsets provide an immersive illusion that blurs the line between reality and the virtual environment, helping subjects to imagine movement better by observing their own virtual-self performing the said motor tasks. The aforementioned advantages have led to VR headsets often being employed in motor imagery–based rehabilitation paradigms.

MI training has been shown to elicit event-related desynchronization (ERD), an attenuation of neuronal rhythms with the alpha (8–13 Hz) and beta (13–25 Hz) frequency bands during imagination of a motor task (Jeon et al., 2011; Mcfarland et al., 2000; Pfurtscheller & Neuper, 1997). Furthermore, kinesthetic motor imagery, or the ability to imagine movement by means of proprioception, elicits an ERD response that is similar to that of actual execution of the motor task (Stinear et al., 2006). Earlier research on kinesthetic MI has implied an overlap in the neuronal circuits between movement imagination and movement execution (Stinear et al., 2006; Toriyama et al., 2018). With its ability to induce neuroplasticity (Toriyama et al., 2018) and the potential to help patients, proprioception-based MI has been the subject of much research to improve its effectiveness in eliciting cortical responses.

Proprioception shares the same population of posterior parietal neurons as tactile information while encoding within high-level spatial representations (Mikula et al., 2018; Rizzolatti et al., 1998). Mikula et al. (2018) showed that providing additional tactile information through vibrotactile stimulation improved proprioception of the hand during a reaching task. Recently, it has been shown that the application of sensory stimulation away from the hand (e.g., wrist, forearm) improves hand tactile sensation in stroke survivors (Enders et al., 2013) and healthy individuals (Lakshminarayanan et al., 2015). The sensory stimulation is applied via a subthreshold imperceptible vibration to the hand of the subject. The vibration-based sensory stimulation has been known to improve tactile sensation and muscle reaction time (Seo et al., 2015, 2019). Specifically, Seo et al. (2019) showed subthreshold imperceptible vibration increase ERD activity in the alpha and beta bands during a motor task. This effect is similar to “stochastic resonance” (Kurita et al., 2013) in which minute, unperceivable noise improves signal detection by elevating the signal amplitude (Lakshminarayanan et al., 2015). A study by Mizuguchi et al. (2009) found that motor imagery, combined with tactile signals from an object, greatly improved the motor-evoked potential. Hence, an enhanced tactile sensation via sensory stimulation might have an influence on motor imagery. However, to date, the combined effect of VR-based MI and vibrotactile stimulation in enhancing MI-related cortical activity is unknown. If found to be effective, the multimodal rehabilitation strategy will help maximize the degree of recovery and shorten recovery time for those who have any type of injury requiring physical rehabilitation.

The purpose of this study was to determine the effect of imperceptible vibrotactile stimulation induced/enhanced tactile sensation on the cortical activity during repeated kinesthetic motor imagery of hand movements. To achieve this, we examined the cortical activity using electroencephalography (EEG) during kinesthetic MI with and without sensory stimulation. Machine learning techniques were applied to discriminate different MI task-based sensorimotor responses. It was hypothesized that sensory stimulation–enhanced tactile sensation would increase the ERD response and task discrimination during VR-based kinesthetic MI compared to without sensory stimulation.

Methods

Subjects

Fifteen healthy right-handed adults (six females and nine males) with a mean age of 28 ± 4 years took part in the study. All subjects verbally disclosed that they had no history of upper-limb injury or musculoskeletal or neurologic disorders. All subjects had no prior experience with motor imagery or VR. The protocol was approved by the Vellore Institute of Technology Review Board. Subjects read and signed a written informed consent form before participating in the experiment.

Procedure

The alpha and beta band ERD at the sensorimotor cortex was evaluated while subjects performed VR-based kinesthetic motor imagery with and without imperceptible vibrotactile stimulation on their right index fingertip.

Sensory Stimulation

The sensory stimulation was applied via an imperceptible vibration. The vibration was applied by attaching a flat vibration micro motor (SunRobotics) on the right-hand index fingertip. The vibration motor generated a band-limited white-noise vibration with frequency ranges from 0.001 to 500 Hz. During the experiment the vibration was applied continuously to the subjects’ index when the motor imagery had to be performed under sensory stimulation on condition. The sensory threshold, which is the lowest vibration intensity the subject could perceive, was determined and the vibration was set to an intensity of 60% of the individual subjects’ sensory threshold at the index fingertip.

EEG Recording

EEG signals were recorded using an Allengers’ Virgo (Allengers Medical Systems) EEG system. An EEG cap with 20 electrodes (FP1, FPz, FP2, F7, F3, Fz, F4, F8, T3, C3, Cz, C4, T4, T5, P3, Pz, P4, T6, O1, and O2), following the international 10–20 system, was placed on the scalp of each subject, with FPz and Fz electrodes serving as ground and reference, respectively. High-quality EEG signals were obtained by keeping each electrode site under an impedance of 5 kΩ by adequate application of conductive gel between the electrode and the scalp. During the experiment, EEG data was recorded continuously at 250 Hz.

Virtual-Reality Environment

A three-dimensional (3D) avatar that resembled each subject was modeled and animated in Blender software (Blender Foundation). The avatars were animated to perform three different right-hand tasks, namely drinking from a cup, grabbing a cup, and flexion–extension of the right-hand wrist. Unity game engine (Unity Technologies) was used to gamify the avatars and their animation inside a 3D virtual environment. Subjects wore an Oculus Rift-S (Oculus VR) VR headset that displayed the graphical scenario with the avatar performing the hand tasks. Subjects wore the VR headset after wearing the EEG cap. The VR headset provided an immersive VR environment to the subjects.

Experimental Design

Subjects performed kinesthetic MI while wearing a VR headset that provided graphical hand task scenarios for the MI practice. Subjects were asked to observe their 3D avatar performing the task and imagine kinesthetically the same movement by forming an impression of their own right hand performing the task in question. A trial run was performed to make the subjects comfortable with imagining the task at the pace it was performed by the avatar.

The experiment was conducted in a quiet room with minimal environmental distractions. Subjects were instructed to sit comfortably in a chair with their arms on the arm rest (Figure 1). During EEG recording, subjects were instructed to avoid any movement including eye blinking to reduce motion artifacts. The experiment consisted of three blocks corresponding to the three right-hand tasks (drinking, grabbing, and flexion–extension). A single block had five sessions consisting of 10 trials of MI per session with adequate rest provided between sessions. A single trial had a 3-second instruction followed by a 4-second MI period, and then a 2-second resting period. During the experiment, subjects repeated each block twice, once with the sensory stimulation being on and once without the stimulation. Subjects were not aware of which blocks had the sensory stimulation on since the vibration was imperceptible. The vibrotactile sensory stimulation was applied continuously throughout the blocks under the sensory stimulation on conditions, as subjects performed the three hand tasks. At the beginning of each session, a countdown of 3 s was displayed before the first MI trial started. The two sensory stimulation conditions (on and off) and the order of the blocks within the sensory stimulation conditions were randomized for each subject. EEG was recorded continuously during an entire block.

EEG Analysis

Preprocessing

The EEG data were band-pass filtered between 8 and 30 Hz and rereferenced to a common average reference. Independent component analysis was performed to remove artifacts. The cleaned data were subdivided into epochs ranging from −1,000 to 6,000 ms relative to the start of each trial. A total of 150 epochs with 50 epochs for each hand task was used for each sensory stimulation condition. EEG preprocessing and analysis was all performed in MATLAB, with ERD analysis performed using EEGLAB toolbox and discrimination analysis performed using an artificial neural network available in the Neural Network toolbox in MATLAB.

ERD Analysis

Time–frequency analysis was performed on the EEG data to obtain the event-related spectral perturbations (ERSP). The ERSP is the dynamic change in frequency amplitude as a function of time. The frequency was linearly spaced between 0 and 40 Hz to include both the alpha band (8–12 Hz) and beta band (13–30 Hz) frequencies, while the time was linearly spaced to 200 time points. ERSP was calculated for each MI trial epoch and baseline corrected to the respective rest period (−1,000 to 0 ms). The individual ERSPs were all averaged for each task and for each participant to obtain an average ERSP. For each hand task under different sensory stimulation conditions, we analyzed the mean ERSP of all subjects in the three EEG channels over the sensorimotor cortex—C3, Cz, and C4.

We further analyzed ERD at the alpha and beta bands from the ERSP for a typical channel, C3, over the contralateral left sensorimotor area. ERD during the MI task manifested as a decrease in the ERSP amplitude during the time period the task was imagined. ERD activity in the alpha and beta bands was calculated by averaging the ERSP values over the two frequency bins corresponding to alpha (8–12 Hz) and beta (13–30 Hz) bands. The ERD was calculated for each task in the two sensory stimulation conditions.

Discriminant Analysis

A pattern recognition neural network was constructed to classify the three tasks. The neural net pattern recognition employed two-layer feedforward networks with a learning function based on random order incremental training. The algorithm had all the features in each epoch presented once in a different random order, while the weight and bias values were updated incrementally after each individual presentation. Feature extraction was performed on the ERSP data to extract discriminating features corresponding to the three tasks. A 3-second time period between 1,000 and 4,000 ms in the epoch when MI was performed, and ERD occurred, was extracted from each trial. The extracted ERSP data were then averaged between 8 and 30 Hz. The input vector to the neural network had a dimension of 200 × 150 representing the 200 time point values and the average ERSP value for the 150 trials (50 trials per task). The target vector represented the labels for the three tasks (drinking, grabbing, and flexion–extension) in a 3 × 150 vector. The neural network had an input layer with 150 neurons (N), an output layer with 3 neurons (M) corresponding to the three task classes, and a hidden layer with 22 neurons that was calculated based on the number of neurons in the input and output layers, using the following formula (Fadiyah & Djamal, 2019):
N×M.
The neural network was trained on 70% of the data and was validated on 15% of the data and finally tested on the remaining 15% of the data. The training and validation data were chosen randomly and so to avoid the influence of the randomly chosen data, the neural network training and testing were repeated 100 times. The accuracy percentages from the 100 runs were averaged to obtain the final classification accuracy percentage.

Statistical Analysis

To examine the effect of sensory stimulation on MI performance, a repeated-measures analysis of variance (ANOVA) was performed on the task-related average ERD for alpha and beta band separately. Specifically, alpha and beta bands ERD was ERSP averaged over the 4-second MI period immediately after the rest cue for each of the two frequency bands. The independent variables included vibration condition (on vs. off), and task (drinking, grabbing, and flexion–extension). Post hoc Bonferroni t tests were used for all pairwise comparisons. Furthermore, a paired t test was performed on the classification accuracy from the two sensory stimulation conditions. The statistical analysis was performed using SigmaStat (version 4.0, Systat Software Inc.). An α level of .05 was considered for statistical significance.

Results

Time–Frequency Analysis of EEG

In the current study, subjects performed trials with periods of kinesthetic MI with and without a vibrotactile sensory stimulation applied to their hand. Figure 2 shows the average time–frequency maps of all subjects at the C3, Cz, and C4 channels for all three hand tasks under both sensory stimulation conditions. The time–frequency maps (Figure 2a) clearly show a long-lasting event-related desynchronization as a decrease in power in the alpha and beta bands from the onset of each of the hand tasks. Compared to without sensory stimulation, the imperceptible vibrotactile stimulation is shown to elicit larger ERD. Furthermore, to study the activation of the sensorimotor area, the average energy distribution in the alpha and beta bands of each channel was calculated and plotted into a topology map based on the channel positions (Figure 2b). The mean EEG potential during the kinesthetic motor imagery of right-hand tasks was topographically located in the contralateral left sensorimotor area consistent with previous studies (Lebon et al., 2012). With vibration on, the potential was more prominent and focused at the contralateral sensorimotor area compared to vibration off, specifically for the drinking and grabbing tasks.

Figure 1
Figure 1

(a) Experimental setup, (b) timeline of a single motor imagery task. EEG = electroencephalography; VR = virtual reality.

Citation: Motor Control 2023; 10.1123/mc.2022-0061

Figure 2
Figure 2

(a) Averaged time–frequency maps of all participants. (b) Averaged topographical distribution of power. Blue indicates ERD. ERD = event-related desynchronization; ERSP = event-related spectral perturbations.

Citation: Motor Control 2023; 10.1123/mc.2022-0061

Statistical analysis was performed to test if the differences among the two vibration conditions were significant. A repeated-measures ANOVA was applied to study the differences between vibration on versus off. First, the assumptions made by the ANOVA on the ERD results were verified. No significant outliers were identified in the data. Furthermore, A Shapiro–Wilk normality test indicated that the results followed a normal distribution, and the normality was not violated for all groups with a p value > .05. Brown–Forsythe test was performed to test if the variances of the differences between vibration conditions are equal. The results revealed equal variances for all groups (p > .05).

ERD at the C3 electrode during motor imagery was compared between the vibration on versus off conditions. In the alpha band, repeated-measures ANOVA showed that ERD did not significantly differ by the main effects, that is, vibration condition (on vs. off; p = .886) or task (drinking, grabbing, and flexion–extension; p = .232). Post hoc Bonferroni tests revealed that ERD between vibration conditions for each task (Figure 3) did not differ significantly for drinking (p = .070), grabbing (p = .080), or flexion–extension (p = .613) tasks.

Figure 3
Figure 3

The grand average ERD (mean ± SD) of all participants from alpha and beta bands for both vibration conditions for each task. A significant difference between the vibration on and off conditions was found for beta band ERD during drinking and grabbing tasks only (noted with *). ERD = event-related desynchronization.

Citation: Motor Control 2023; 10.1123/mc.2022-0061

In the beta band, the repeated-measures ANOVA showed ERD significantly differing between the vibration conditions (p = .040) but not by task (p = .750). The interactions between the vibration conditions and tasks were significant (p = .020). The ERD showed a significant increase with vibration on (mean ± SD = −3.54 ± 1.18 dB) compared to vibration off (mean ± SD = −2.91 ± 1.00 dB). ERD between vibration on versus off for each task (Figure 3) evaluated by post hoc Bonferroni tests revealed that ERD was significantly greater with vibration on compared to off for both drinking (p = .048) and grabbing (p = .005) but not during flexion–extension (p = .388).

Task Classification Analysis

A task discrimination analysis was performed between the vibration on and off conditions using a neural network. The neural network tried to classify the three tasks for each vibration condition and gave a classification accuracy based on the percentage of correctly classified tasks. The classification percentage for vibration on and off are shown in Table 1. A paired t test was performed to evaluate the statistical difference between the classification accuracies from the vibration conditions. The results showed that MI-BCI classification performance during vibration on (78.49 ± 4.37%) were significantly higher (p < .01) than vibration off (73.45 ± 3.92%).

Table 1

Classification Percentage for Each Subject

SubjectVibration on (%)Vibration off (%)
187.5577.46
277.1675.46
368.4368.49
480.1063.66
577.1878.10
679.9672.73
779.8672.78
884.7979.41
977.5573.61
1079.6374.03
1178.1274.96
1277.0171.33
1373.1672.05
1479.2971.99
1577.5175.73

Discussion

The current study investigated the effect of enhanced tactile sensation via imperceptible vibrotactile stimulation during repeated kinesthetic motor imagery in an immersive VR graphical scenario on the MI-induced EEG activity and MI-BCI classification performance in healthy adults. By comparing the EEG power during motor imagery in the alpha and beta bands and the neural network–based task discrimination results from our study, we have provided evidence that vibrotactile stimulation-based enhanced tactile sensation does influence the cortical activity induced by MI and the discriminability of tasks performed by the same given limb compared to without stimulation. It has been extensively documented in literature how the reduction in EEG power presents itself as an event-related desynchronization in the alpha and beta band during the period of motor imagery (Pfurtscheller & Neuper, 1997). The presence of an identifiable pattern of ERD is an indicator of performance of MI. In terms of the repeated motor imagery practice, we have not only confirmed that VR-based motor imagery resulted in ERD in the alpha and beta bands during the period of the motor imagery in all the subjects (Alimardani et al., 2016; Juliano et al., 2020; Penaloza et al., 2018; Pozeg et al., 2017; Škola & Liarokapis, 2018; Slater, 2017), but we also discovered that adding a sensory stimulation further enhances the ERD activity. Based on the task discrimination results from our study, it has been confirmed that using sensory stimulation to enhance tactile sensation is more effective at improving decoding of the tasks performed by the same given limb compared to performing MI without stimulation.

ERD during repeated kinesthetic motor imagery of right-handed tasks was examined from the contralateral left motor cortex with and without an imperceptible vibrotactile stimulation applied to the fingertip in our study. The enhanced tactile sensation via vibrotactile stimulation showed a greater ERD in the beta band compared to without stimulation. These results are similar to findings reported in an earlier study where a greater ERD was elicited by the vibrotactile sensory stimulation in the beta band during two different pinch tasks (Seo et al., 2019). With beta band activity reflecting motor tasks (Khanna & Carmena, 2015), the results from the previous study (Seo et al., 2019) and results from our study suggests that enhanced tactile sensation via sensory stimulation has a similar effect on the ERD during both motor execution and motor imagery. A possible explanation could be that the enhanced tactile sensation led to an enhanced proprioception, or the on-line representation of the hand in space for the subjects, helping them to better imagine the hand motor tasks kinesthetically and thereby improving MI-BCI performance. Previous studies have shown that imperceptible vibration enhances tactile sensation (Lakshminarayanan et al., 2015; Seo et al., 2015; Seo et al., 2019), and tactile stimulation improves proprioception of the hand (Mikula et al., 2018; Rizzolatti et al., 1998), possibly because tactile sensation and proprioception share the same population of posterior parietal neurons during high-level spatial representations (Rizzolatti et al., 1998). The ERD effect with vibration was spread over the primary motor cortex and the somatosensory area (Figure 3), suggesting that the sensory stimulation was received at the somatosensory area and processed further in the primary motor cortex, as it is well documented that sensory feedback reaches the motor cortex too and influences the discharge of corticospinal cells (Cheney & Fetz, 1984; Lemon, 1981). Furthermore, a study by Shenton et al. (2004) showed that motor imagery of hand rotation was significantly affected by proprioceptive input rather than visual input. In the current study, the enhanced tactile sensation via imperceptible vibration has only been shown to enhance the ERD response significantly for the drinking and grabbing tasks but not the hand flexion–extension tasks. A possible explanation could be that both the drinking and grabbing tasks required some level of target reaching with the drinking task involving bringing a cup toward the mouth while the grabbing task required grabbing a cup placed on a table and letting go. Such target-based tasks require more proprioceptive information about the limb’s position in 3D space than the flexion–extension task. With the imperceptible vibration-enhancing proprioception, it is possible that the drinking and grabbing tasks might be more influenced by the enhanced proprioception compared to the flexion–extension task.

The imperceptible vibration not only affected EEG power but also task discrimination using a neural network. The current study used ERD during motor imagery of three right hand tasks (drinking, grabbing, and flexion–extension) as a feature to classify the three tasks with and without vibration during the motor imagery practice. The vibration on condition showed a higher classification accuracy compared to no vibration applied, leading to a better MI-BCI classification performance with sensory stimulation. A high task classification accuracy from a single given limb has implications in brain–computer interface and controlling prosthetic arms. The major component that contributed to the enhancement in classification performance is the increased spatial resolution of MI-induced cortical activity with the sensory stimulation. Future studies can focus on further improving the spatial resolution, such as MI between different digits in the same given hand.

There are some limitations in the current study. The study has a relatively small sample size, and although the trials were repeated, subjects still showed variation in their performance between each other that may have affected the statistical power. Thus, the results from the current study need to be interpreted carefully. Furthermore, although the task classification performance was significantly improved with the sensory stimulation, the improvement seen is modest (approximately 5%). Since neural networks require memory heavy computations, we had to restrict ourselves to including only one electrode in the algorithm. Future studies will focus on recruiting more electrodes and more neural activity features for a higher classification performance. Finally, we did not use any questionnaire to evaluate attention level differences between the two sensory stimulation conditions. However, this was because the vibration was imperceptible, and the subjects were not made aware when the vibration was on to avoid any bias in performance of the MI.

Conclusions

In summary, the current study showed that imperceptible random frequency vibration applied to the fingertip during kinesthetic motor imagery led to an increase in task-related ERD activity, indicating activity of the sensorimotor cortex and greater task discrimination. These effects are similar to those seen in previous studies with motor tasks and suggest that the positive effects of the sensory stimulation in enhancing motor function can be extended to motor imagery as well. The modality of using a subthreshold imperceptible vibration that is mobile has advantages in motor learning through VR-based graphical scenarios and kinesthetic motor imagery.

Acknowledgments

The data sets used and/or analyzed during the current study are available from the corresponding author on reasonable request. This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

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  • Pfurtscheller, G., & Neuper, C. (1997). Motor imagery activates primary sensorimotor area in humans. Neuroscience Letters, 239(2–3), 65–68. https://doi.org/10.1016/S0304-3940(97)00889-6

    • PubMed
    • Search Google Scholar
    • Export Citation

  • Pozeg, P., Palluel, E., Ronchi, R., Solcà, M., Al-Khodairy, A.W., Jordan, X., . . . Blanke, O. (2017). Virtual reality improves embodiment and neuropathic pain caused by spinal cord injury. Neurology, 89(18), 1894–1903. https://doi.org/10.1212/WNL.0000000000004585

    • PubMed
    • Search Google Scholar
    • Export Citation

  • Rizzolatti, G., Luppino, G., & Matelli, M. (1998). The organization of the cortical motor system: New concepts. Electroencephalography and Clinical Neurophysiology, 106(4), 283–296. https://doi.org/10.1016/S0013-4694(98)00022-4

    • PubMed
    • Search Google Scholar
    • Export Citation

  • Schuster, C., Hilfiker, R., Amft, O., Scheidhauer, A., Andrews, B., Butler, J., . . . Ettlin, T. (2011). Best practice for motor imagery: A systematic literature review on motor imagery training elements in five different disciplines. BMC Medicine, 9(1), 1–35. https://doi.org/10.1186/1741-7015-9-75

    • Search Google Scholar
    • Export Citation

  • Seo, N.J., Lakshminarayanan, K., Bonilha, L., Lauer, A.W., & Schmit, B.D. (2015). Effect of imperceptible vibratory noise applied to wrist skin on fingertip touch evoked potentials—An EEG study. Physiological Reports, 3(11), Article 12624. https://doi.org/10.14814/phy2.12624

    • Search Google Scholar
    • Export Citation

  • Seo, N.J., Lakshminarayanan, K., Lauer, A.W., Ramakrishnan, V., Schmit, B.D., Hanlon, C.A., . . . Nagy, T. (2019). Use of imperceptible wrist vibration to modulate sensorimotor cortical activity. Experimental Brain Research, 237(3), 805–816. https://doi.org/10.1007/s00221-018-05465-z

    • PubMed
    • Search Google Scholar
    • Export Citation

  • Shenton, J.T., Schwoebel, J., & Coslett, H.B. (2004). Mental motor imagery and the body schema: Evidence for proprioceptive dominance. Neuroscience Letters, 370(1), 19–24. https://doi.org/10.1016/j.neulet.2004.07.053

    • PubMed
    • Search Google Scholar
    • Export Citation

  • Škola, F., & Liarokapis, F. (2018). Embodied VR environment facilitates motor imagery brain–computer interface training. Computers & Graphics, 75, 59–71. https://doi.org/10.1016/j.cag.2018.05.024

    • Search Google Scholar
    • Export Citation

  • Slater, M. (2017). Implicit learning through embodiment in immersive virtual reality. In D. Liu, C. Dede, R. Huang, & J. Richards. (Eds.), Virtual, augmented, and mixed realities in education (pp. 19–33). Springer.

    • Search Google Scholar
    • Export Citation

  • Stinear, C.M., Byblow, W.D., Steyvers, M., Levin, O., & Swinnen, S.P. (2006). Kinesthetic, but not visual, motor imagery modulates corticomotor excitability. Experimental Brain Research, 168(1), 157–164. https://doi.org/10.1007/s00221-005-0078-y

    • Search Google Scholar
    • Export Citation

  • Toriyama, H., Ushiba, J., & Ushiyama, J. (2018). Subjective vividness of kinesthetic motor imagery is associated with the similarity in magnitude of sensorimotor event-related desynchronization between motor execution and motor imagery. Frontiers in Human Neuroscience, 12, Article 295. https://doi.org/10.3389/fnhum.2018.00295

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    • Search Google Scholar
    • Export Citation

  • Turolla, A., Dam, M., Ventura, L., Tonin, P., Agostini, M., Zucconi, C., . . . Piron, L. (2013). Virtual reality for the rehabilitation of the upper limb motor function after stroke: A prospective controlled trial. Journal of Neuroengineering and Rehabilitation, 10(1), 85–89. https://doi.org/10.1186/1743-0003-10-85

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Motor Control

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  • Figure 1
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    Figure 1

    (a) Experimental setup, (b) timeline of a single motor imagery task. EEG = electroencephalography; VR = virtual reality.

  • Figure 2
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    Figure 2

    (a) Averaged time–frequency maps of all participants. (b) Averaged topographical distribution of power. Blue indicates ERD. ERD = event-related desynchronization; ERSP = event-related spectral perturbations.

  • Figure 3
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    Figure 3

    The grand average ERD (mean ± SD) of all participants from alpha and beta bands for both vibration conditions for each task. A significant difference between the vibration on and off conditions was found for beta band ERD during drinking and grabbing tasks only (noted with *). ERD = event-related desynchronization.

Figure 1

(a) Experimental setup, (b) timeline of a single motor imagery task. EEG = electroencephalography; VR = virtual reality.
Figure 2

(a) Averaged time–frequency maps of all participants. (b) Averaged topographical distribution of power. Blue indicates ERD. ERD = event-related desynchronization; ERSP = event-related spectral perturbations.
Figure 3

The grand average ERD (mean ± SD) of all participants from alpha and beta bands for both vibration conditions for each task. A significant difference between the vibration on and off conditions was found for beta band ERD during drinking and grabbing tasks only (noted with *). ERD = event-related desynchronization.

  • Alimardani, M., Nishio, S., & Ishiguro, H. (2016). The importance of visual feedback design in BCIs; from embodiment to motor imagery learning. PLoS One, 11(9), Article 0161945. https://doi.org/10.1371/journal.pone.0161945

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  • Cameirao, M.S., Badia, S.B.I., Duarte, E., Frisoli, A., & Verschure, P.F. (2012). The combined impact of virtual reality neurorehabilitation and its interfaces on upper extremity functional recovery in patients with chronic stroke. Stroke, 43(10), 2720–2728. https://doi.org/10.1161/STROKEAHA.112.653196

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  • Fadiyah, A.U., & Djamal, E.C. (2019). Classification of motor imagery and synchronization of post-stroke patient EEG signal. Paper presented at the 2019 6th International Conference on Electrical Engineering, Computer Science and Informatics (EECSI; pp. 28–33). IEEE.

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  • Juliano, J.M., Spicer, R.P., Vourvopoulos, A., Lefebvre, S., Jann, K., Ard, T., . . . Liew, S.L. (2020). Embodiment is related to better performance on a brain–computer interface in immersive virtual reality: A pilot study. Sensors, 20(4), Article 1204. https://doi.org/10.3390/s20041204

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  • Penaloza, C.I., Alimardani, M., & Nishio, S. (2018). Android feedback-based training modulates sensorimotor rhythms during motor imagery. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 26(3), 666–674. https://doi.org/10.1109/TNSRE.2018.2792481

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  • Pfurtscheller, G., & Neuper, C. (1997). Motor imagery activates primary sensorimotor area in humans. Neuroscience Letters, 239(2–3), 65–68. https://doi.org/10.1016/S0304-3940(97)00889-6

    • PubMed
    • Search Google Scholar
    • Export Citation

  • Pozeg, P., Palluel, E., Ronchi, R., Solcà, M., Al-Khodairy, A.W., Jordan, X., . . . Blanke, O. (2017). Virtual reality improves embodiment and neuropathic pain caused by spinal cord injury. Neurology, 89(18), 1894–1903. https://doi.org/10.1212/WNL.0000000000004585

    • PubMed
    • Search Google Scholar
    • Export Citation

  • Rizzolatti, G., Luppino, G., & Matelli, M. (1998). The organization of the cortical motor system: New concepts. Electroencephalography and Clinical Neurophysiology, 106(4), 283–296. https://doi.org/10.1016/S0013-4694(98)00022-4

    • PubMed
    • Search Google Scholar
    • Export Citation

  • Schuster, C., Hilfiker, R., Amft, O., Scheidhauer, A., Andrews, B., Butler, J., . . . Ettlin, T. (2011). Best practice for motor imagery: A systematic literature review on motor imagery training elements in five different disciplines. BMC Medicine, 9(1), 1–35. https://doi.org/10.1186/1741-7015-9-75

    • Search Google Scholar
    • Export Citation

  • Seo, N.J., Lakshminarayanan, K., Bonilha, L., Lauer, A.W., & Schmit, B.D. (2015). Effect of imperceptible vibratory noise applied to wrist skin on fingertip touch evoked potentials—An EEG study. Physiological Reports, 3(11), Article 12624. https://doi.org/10.14814/phy2.12624

    • Search Google Scholar
    • Export Citation

  • Seo, N.J., Lakshminarayanan, K., Lauer, A.W., Ramakrishnan, V., Schmit, B.D., Hanlon, C.A., . . . Nagy, T. (2019). Use of imperceptible wrist vibration to modulate sensorimotor cortical activity. Experimental Brain Research, 237(3), 805–816. https://doi.org/10.1007/s00221-018-05465-z

    • PubMed
    • Search Google Scholar
    • Export Citation

  • Shenton, J.T., Schwoebel, J., & Coslett, H.B. (2004). Mental motor imagery and the body schema: Evidence for proprioceptive dominance. Neuroscience Letters, 370(1), 19–24. https://doi.org/10.1016/j.neulet.2004.07.053

    • PubMed
    • Search Google Scholar
    • Export Citation

  • Škola, F., & Liarokapis, F. (2018). Embodied VR environment facilitates motor imagery brain–computer interface training. Computers & Graphics, 75, 59–71. https://doi.org/10.1016/j.cag.2018.05.024

    • Search Google Scholar
    • Export Citation

  • Slater, M. (2017). Implicit learning through embodiment in immersive virtual reality. In D. Liu, C. Dede, R. Huang, & J. Richards. (Eds.), Virtual, augmented, and mixed realities in education (pp. 19–33). Springer.

    • Search Google Scholar
    • Export Citation

  • Stinear, C.M., Byblow, W.D., Steyvers, M., Levin, O., & Swinnen, S.P. (2006). Kinesthetic, but not visual, motor imagery modulates corticomotor excitability. Experimental Brain Research, 168(1), 157–164. https://doi.org/10.1007/s00221-005-0078-y

    • Search Google Scholar
    • Export Citation

  • Toriyama, H., Ushiba, J., & Ushiyama, J. (2018). Subjective vividness of kinesthetic motor imagery is associated with the similarity in magnitude of sensorimotor event-related desynchronization between motor execution and motor imagery. Frontiers in Human Neuroscience, 12, Article 295. https://doi.org/10.3389/fnhum.2018.00295

    • PubMed
    • Search Google Scholar
    • Export Citation

  • Turolla, A., Dam, M., Ventura, L., Tonin, P., Agostini, M., Zucconi, C., . . . Piron, L. (2013). Virtual reality for the rehabilitation of the upper limb motor function after stroke: A prospective controlled trial. Journal of Neuroengineering and Rehabilitation, 10(1), 85–89. https://doi.org/10.1186/1743-0003-10-85

    • PubMed
    • Search Google Scholar
    • Export Citation
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