The authors are grateful to Dr. Margaret Wardle for her exceptional training and technical assistance. This work was funded in part by Swedish Research Council grant FYF-2013-687 (IM).

This protocol is based on Mayo et al.31 (Experiment 1) and Ree et al.32 (Experiment 2). Ethical approval was granted by the Regional Ethical Review Board, Link&#246;ping, Sweden (Experiment 1) and the local ethical committee at the Department of Psychology, University of Oslo, Norway (Experiment 2).
1. Participant Screening and Preparation
<ol>
	<li>Recruit participants who lack tactile or uncorrected visual disturbances and are free of any neurological or psychiatric disorder, unless a specific patient population is being recruited.</li>
	<li>Ensure that participants are fully able to understand task instructions (e.g., fluent in the language that tasks are administered).</li>
	<li>If including more than one task (e.g. Experienced, Observed), ensure that task order is counterbalanced across participants, stratifying for gender, age, or other distinguishing factors.</li>
</ol>
2. Stimuli and Task Construction
NOTE: See Table 1 for experimental design.
<ol>
	<li>Experienced touch task (Experiments 1 and 2)
	<ol>
		<li>Create trials such that they consist of a baseline period, touch administration, and self-report ratings, all separated by jittered ITIs.
		<ol>
			<li>Baseline periods consist of a blank screen, fixation cross, or other neutral scene prior to tactile stimulation.</li>
			<li>Tactile stimulation is followed by a short (e.g. 1-2 s) ITI, then self-report ratings are obtained.</li>
			<li>A jittered inter-trial interval (ITI; e.g. 6-7 s) follows self-report ratings to allow muscle activity to return to baseline levels before the next trial begins.</li>
		</ol>
		</li>
		<li>Use either audio (Experiment 131) or visual (Experiment 232) cues to ensure that touch is delivered at the appropriate velocity.
		<ol>
			<li>To use audio cues, have cues delivered to headphones worn by the experimenter to track the pace of the stimulation using a metronome. Distinguish velocities using tones of differing pitches (or other distinguishing audio cue, e.g., a cue saying &#34;10 cm/s&#34;) that precede the stimulation cues.</li>
			<li>To use visual cues, display cues on a tablet only in view of the experimenter. Use a moving bar to track velocity of touch administration.</li>
		</ol>
		</li>
		<li>Prior to the start of the study, practice to ensure that touch is delivered at the appropriate velocity and a consistent pressure. To do so, apply brushstrokes to the scale in a similar manner as to the participant. The scale readout is used to determine if the pressure changes throughout touch administration. For instance, a pressure of 0.4 N would read as 40 g on the scale.</li>
	</ol>
	</li>
	<li>Observed touch task (Experiment 1)
	<ol>
		<li>Ensure that videos of touch administration are of similar length, regardless of velocity.
		<ol>
			<li>Include both CT-optimal (1-10 cm/s) and non-optimal (less than 1 cm/s or more than 10 cm/s) velocities.</li>
		</ol>
		</li>
		<li>Start trials with a fixation cross or other neutral condition followed by video. 
		NOTE: Videos contain touch delivered to CT-rich hairy skin (arm), CT-lacking glabrous skin (palm), and a non-social condition in which touch is delivered to a fake wooden arm (Fig. 2; see supplemental videos).
		<ol>
			<li>After a 1-2 s ITI, obtain self-report ratings.</li>
			<li>Allow another 6-7 ITI following ratings to precede the next trial to allow EMG activity to return to baseline.</li>
		</ol>
		</li>
	</ol>
	</li>
</ol>
3. Facial Electromyography
<ol>
	<li>Data acquisition and filtering guidelines (based on previous protocols27,34)
	<ol>
		<li>Use software to apply filtering steps either in real-time or offline. Typical filtering steps include a comb band stop filter to filter out potential noise from AC power (50/60 Hz), followed by smoothing and rectification. 
		NOTE: Initial basic filtering steps may be set on EMG amplifiers (e.g., a high pass filter of 10 Hz and a low pass filter of 500 Hz or 1,000 Hz).</li>
	</ol>
	</li>
	<li>Electrode application (based on previous protocols27,34)
	<ol>
		<li>Briefly describe the application process to the participant. Use neutral words (&#34;sensor&#34;) instead of potentially anxiety-evoking words (&#34;electrode&#34;)34.
		<ol>
			<li>Decide what information to tell the participants regarding the purpose of the sensors. 
			NOTE: In the current studies, participants were told sensors would measure muscle and sweat activity during the session.</li>
		</ol>
		</li>
		<li>Clean the participants&#39; skin prior to electrode application.
		<ol>
			<li>Use water to wipe clean the areas in which sensors will be applied.</li>
			<li>Use an exfoliant scrub to lightly abrade the same areas. Use caution to prevent major skin irritation, though minor irritation is likely to occur.</li>
		</ol>
		</li>
		<li>Use electrode pairs consisting of two 4 mm shielded bipolar recording electrodes plus one monopolar reference electrode.
		<ol>
			<li>Apply adhesive collars to the electrodes such that they adhere to the skin.</li>
			<li>Once collars adhere to the outer rim of the electrodes, fill sensors with a conductive electrode gel, taking care to prevent the formation of air bubbles.</li>
		</ol>
		</li>
		<li>Place electrode pairs parallel to the muscle(s) of interest and perpendicular to potential sources of noise, such as other muscles34.
		<ol>
			<li>Corrugator: Affix one electrode directly above the eyebrow along an imaginary vertical line that traverses the inner corner of the eye. Place the second electrode 1 cm lateral and slightly superior to the first, along the border of the eyebrow.</li>
			<li>Zygomatic: Place the first sensor midway along an imaginary line that connects the upper ear (where the ear meets the skull) and the corner of the mouth. Place the second electrode 1 cm medial (towards the mouth). Take care to avoid the masseter muscle.</li>
			<li>Use an 8 mm unshielded, monopolar recording electrode as a reference electrode. Place the electrode in the middle of the forehead, equidistant (above) the inner brows and (below) the hairline.</li>
			<li>Ensure that electrode wires are placed such that they do not impede vision. Use medical tape to ensure long-term adherence of the electrodes to skin and reduce noise/artifacts due to cord movement.</li>
		</ol>
		</li>
		<li>Determine the quality of electrode application with an impedance monitor. Acceptable impedance levels are below 20 k&#8486;. If electrodes need to be reapplied to reach appropriate impedance levels, use a clean pair of electrodes.</li>
	</ol>
	</li>
</ol>
4. Task Procedure
<ol>
	<li>General order
	<ol>
		<li>Following sensor application, complete task(s). If using more than one task, counterbalance order across participants.</li>
		<li>Ensure that participants are seated comfortably to minimize extraneous movement that may introduce movement artifacts34.</li>
	</ol>
	</li>
	<li>Experienced touch task
	<ol>
		<li>Seat participants in front of computer with the to-be-touched arm extended laterally, resting comfortably (e.g., on a cushion). 
		NOTE: It is recommended to apply touch to the arm that is not being used for self-reported ratings in order to minimize potential movement artifacts in the EMG signal.</li>
		<li>Occlude view of the arm from the participant either using a curtain separator31 or goggles that occlude lateral vision (Figure 132) 35.</li>
		<li>Instruct the participant to focus on how the touch makes them feel.</li>
		<li>Vary touch location to avoid CT fatigue36.</li>
		<li>Administer touch using a 75 mm goat hair brush applied to designated section(s) marked on the arm (and palm). Alternatively, apply touch using a force-controlled robot37.</li>
		<li>Use consistent touch administration direction, e.g., back-and-forth (distal-to-proximal, then proximal to distal) or single direction (proximal-to-distal only)</li>
	</ol>
	</li>
	<li>Observed touch task
	<ol>
		<li>Seat participant in front of the computer that will display the videos.</li>
		<li>Instruct the participant that they will have to rate how the video made them feel.</li>
		<li>Ensure that the participant is out of view of the experimenter34.</li>
	</ol>
	</li>
</ol>
5. Data Cleaning and Analysis
<ol>
	<li>To assess the mean EMG activation to a specific touch stimulus type, compare the response to the touch stimulus to the preceding baseline, i.e. [mean activation during 6 s touch stimulation] - [mean activation during 1 s prestimulus &#34;baseline&#34;], as suggested by Fridlund and Cacioppo34.
	<ol>
		<li>Average responses for each touch stimulus type (CT-optimal, non-optimal and, if appropriate, each location (arm/palm).</li>
		<li>Do this for each muscle (corrugator, zygomatic) and self-report rating (pleasantness, intensity) individually.</li>
	</ol>
	</li>
	<li>To obtain a more sensitive time course, compute mean EMG activation during smaller time intervals (e.g., 700 ms; see Figure 532). Subtract the same 1 s baseline from all intervals to remove baseline EMG activity. 
	NOTE: Prior to analysis, it is recommended to have data manually checked by raters blinded to touch conditions to eliminate trials with artifactual activations34.</li>
</ol>

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C. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=C-tactile+afferent+stimulating+touch+carries+a+positive+affective+value.">C-tactile afferent stimulating touch carries a positive affective value.</a> PloS One. 12 (3), 0173457 (2017).</li><li>Scheele, D., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=An+oxytocin-induced+facilitation+of+neural+and+emotional+responses+to+social+touch+correlates+inversely+with+autism+traits.">An oxytocin-induced facilitation of neural and emotional responses to social touch correlates inversely with autism traits.</a> Neuropsychopharmacology. 39 (9), 2078-2085 (2014).</li><li>Ackerley, R., Saar, K., McGlone, F., Backlund Wasling, H. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Quantifying+the+sensory+and+emotional+perception+of+touch:+differences+between+glabrous+and+hairy+skin.">Quantifying the sensory and emotional perception of touch: differences between glabrous and hairy skin.</a> Frontiers in Behavioral Neuroscience. 8 (34), (2014).</li><li>Loken, L. S., Evert, M., Wessberg, J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Pleasantness+of+touch+in+human+glabrous+and+hairy+skin:+order+effects+on+affective+ratings.">Pleasantness of touch in human glabrous and hairy skin: order effects on affective ratings.</a> Brain Research. 1417, 9-15 (2011).</li></ol>

The authors have nothing to disclose

Here, we report on the use of facial electromyography (EMG) as a method to study affective responses to observed and experienced touch. Previously, many studies have focused on the use of self-report ratings to characterize the affective quality of touch. Touch that optimally activates CT afferents (e.g., 1-10 cm/s) is consistently rated as more pleasant than either faster or slower touch velocities10. In contrast, ratings of intensity seem to track with velocity, with faster touch velocities rated as more intense, likely mediated via A&#946; fibers37. Using two separate datasets, we show that both fast and slow non-optimal touch elicit robust corrugator reactivity that is attenuated during CT-optimal touch. Thus, we find that touch that is rated as less pleasant (e.g., non-optimal touch) also increases corrugator activity, suggestive of enhanced negative affect. In addition, we find that responses are similar across modalities. That is, both observed and experienced touch elicit similar facial muscle activity. In both modalities, these effects were only significant for touch to the arm, and not the palm or a wooden arm. Thus, while self-reported ratings of experienced and observed affective touch are similar regardless of location (arm, palm), facial EMG only significantly differentiates between touch velocities applied to the CT-rich arm, and not the CT-fiber-lacking palm.
The results further show that the temporal sensitivity of facial EMG yields insight into emotional processing that cannot be obtained solely by self-report. Namely, we found that corrugator reactivity to CT-optimal touch becomes evident at a timescale that coincides with known conduction velocities of CT afferents1,12. Thus, in the initial 700 ms of touch, which are believed to be dominated by A&#946; activation, there is no difference in EMG activation between the two touch velocities. However, the distinction between CT-optimal and non-optimal touch becomes evident following the first 700 ms, consistent with the previously reported temporal time lag of CT-afferents2,12. Hence, facial EMG is able to detect changes in affective responses to touch that occur with a temporal specificity that is likely inaccessible via verbal reporting.
Across both studies, we find that CT-optimal and non-optimal touch can be distinguished via corrugator activity. However, we did not find an effect of touch on zygomatic reactivity, which is in contrast to previous reports40. One potential reason for the discrepancies between the current data and previous findings include methodological differences such as inclusion of a post-touch period in the analysis. Thus, we stress the importance of methodological considerations such as the length of the touch stimulation and inter-trial intervals when designing these experiments.
There are several factors that should be considered when assessing affective reactions to touch. One potential area of concern is the gender of the experimenter (and thus, toucher) to that of the participant, as well as the relationship, if any, between the two41. Moreover, one should ensure that participants are precluded from viewing the experimenter and touch application, as visual processing of touch can influence the perception of touch35,39. There are also concerns to weigh during task design. For instance, it is important to consider the potential for order effects, both in regards to touch stimuli presentation (e.g. discussed in42) or touch location43. If several touch repetitions are used, one may want to vary touch location to avoid CT fatigue36. Here, we used a brush to apply touch to compare to previous studies17, though it is possible that EMG responses may be different using more ecologically valid methods (e.g., touch by hand).
While we believe the use of facial EMG will be of a great benefit to the field of affective touch, there are limitations to this methodology that warrant consideration. Training is required to learn how to apply the electrodes correctly, producing an increased burden on the experimenter on the outset of experimental planning. Excessive movement, talking, or other environmental factors present during the experiment may cause artifacts in the EMG signal, thus constraining some experimental design features. Moreover, the application of electrodes to the face may elicit an attempt to discern the purpose of the study. As such, one must consider what information to tell the participant regarding not only the purpose of the experiment, but also the use of the electrodes during the experiment. In the current experiments, the participants were told that the purpose of the study was the investigate decision-making and perceptions of various sensations32 or reactions to social interactions31. In both cases, participants were told that the electrodes would measure sweat and muscle activity and were fully debriefed following the conclusion of the experiment. These concerns and others are addressed thoroughly in Fridlund and Cacioppo 198634.
In sum, we demonstrate that facial EMG is a reliable, robust, and informative method to assess the affective valence of tactile stimulation. This method provides a means to implicitly assess responses to tactile stimulation independent of verbal reports, paving the way for studies in infants and young children, cross-cultural comparisons, investigations of clinical conditions, and other situations in which semantics and language may otherwise preclude scientific exploration.

C-tactile (CT) afferents are proposed to convey the affective component of touch, which can be distinguished from the discriminative aspects of touch processed via A&#946; fibers1,2. CT-mediated affective touch is believed to play an integral role in social affiliative behaviors3, leading to the &#34;skin as a social organ&#34; hypothesis4. Physical5,6, developmental7, and psychiatric8,9 factors can influence CT-mediated touch processing. Thus, establishing an objective measure to quantify affective reactions to CT-relevant touch is critical to allow for comparisons across populations.
In recent years, much insight has been gained regarding the characteristics of CT afferents. These unmyelinated afferents demonstrate an inverted U-shaped firing frequency, with velocities of 1-10 cm/s (&#34;CT-optimal&#34;) eliciting the greatest frequency and both greater (&#34;fast non-optimal&#34;) or lesser (&#34;slow non-optimal&#34;) velocities eliciting reduced firing10. CT firing frequency correlates with self-reported ratings of touch &#34;pleasantness&#34;, producing a similar inverted U-shaped curve in pleasantness ratings10. Moreover, CT-afferents also respond most robustly to stimuli close to skin temperature11. These fibers also show distinct conduction speeds. The unmyelinated CT afferents are slower2 and thus the volley of afferent input to the cortex shows a temporal lag when compared to the speed of the faster, myelinated A&#946; fibers1,12. Affective and discriminative touch can also be distinguished on a neural level. While both types of touch activate overlapping somatosensory areas, affective touch is more likely to activate the posterior insula, while discriminative touch activates sensorimotor areas13,14,15,16. This activation pattern is consistent whether the touch is directly experienced or merely observed17, suggesting that affective touch is not just a &#34;bottom-up&#34; process driven by physical activation of CT afferents, but also involves &#34;top-down&#34; integration of multimodal sensory processing.
Situations in which CT processing is deficient or otherwise atypical has also provided insight into the functional significance of these afferents. In a unique patient group with a heritable mutation affecting the nerve growth factor &#946; gene, there is a reduction in the density of thin and unmyelinated nerve fibers, including CT afferents. Compared to healthy controls, these patients report touch at CT-optimal velocities as less pleasant5. The converse scenario is also true; patients who lack myelinated A&#946; fibers are able to retain a faint sensation of pleasant touch carried by the still intact CT afferents6. Abnormal affective touch processing is not just confined to instances of physical changes in CT-afferents. Across patient and healthy populations, those higher on the spectrum of autistic traits reported reduced pleasantness ratings of touch8. Psychiatric patients also demonstrate reduced hedonic ratings of affective touch, with a history of childhood maltreatment as one of the most consistent predictors of dysregulated affective touch awareness8. Dysregulation in the CT-based affective touch system in anorexia nervosa has also been reported9. Thus, both physical and psychological factors can influence affective touch processing, and as such, it is imperative to establish methodologies that can be applied to all individuals in an equitable and comparable manner.
Insights into normo-typical and dysregulated affective processing have the opportunity to provide a more nuanced picture of many patient groups. However, one potential limitation of affective touch research is the necessity of self-reported ratings. At times, self-report can be unreliable18 and subject to recall bias19. Inquiries of self-report can psychologically remove a participant from the current setting, limiting the ecological validity of the responses and removing them temporally from the experience20. Moreover, self-report relies on a firm understanding of language and semantics, making cross-cultural and developmentally diverse (e.g. infant and toddler-aged individuals) comparisons challenging. For instance, individuals with an autism spectrum diagnosis frequently show distinct behavioral responses to touch21, but can also have difficulties in communicating verbally22. Thus, finding non-invasive methods to measure responses to touch that circumvent a reliance on self-report may translate, at least, to a better understanding of the mechanisms of affective touch, and at most, novel insights into dysregulation of social processing in patient populations.
Facial electromyography (EMG) is a suitable candidate to objectively assess affective responses to touch. It has been used to measure valence-specific reactions to visual23, audio-visual24, olfactory25, and gustatory26 stimuli. Facial EMG is a safe and non-invasive method consisting of surface electrodes that adhere to the face27. These surface electrodes record facial muscle activity continuously in real-time with time scale sensitivity in the tens of milliseconds. Of particular interest is the corrugator supercilii (&#34;corrugator&#34;), which is activated when furrowing the brow and relaxes during a smile. As a result, corrugator activity has a linear relationship with affective valence, with increased response to negative stimuli and decreased activity in response to positive stimuli28. In addition, the zygomaticus major (&#34;zygomatic&#34;) is the muscle activated as the corners of the mouth pull up into a smile. The zygomatic displays a &#34;J-shaped&#34; activation pattern with positive stimuli eliciting the greatest response, and the most negative stimuli eliciting a greater response than neutral stimuli28. Facial EMG recordings of these muscles can even be observed when stimuli are presented outside conscious awareness or when individuals are explicitly trying to suppress their reactions29,30. Importantly, facial EMG can be used alone or in combination with self-report ratings or other physiological recordings. Thus, it is an ideal methodology to assess affective reactions to tactile stimulation31,32.
In sum, facial EMG can be combined with self-report ratings to determine how CT-optimal tactile stimulation influences facial muscle activity as a potential indicator of affective response. One can take advantage of the velocity-dependent firing frequency of CTs to apply touch at CT-optimal and non-optimal velocities, and touch can be applied both to the CT-rich arm and the putatively CT-lacking palm. Comparisons can be made across modalities to determine whether affective responses to touch require direct stimulation or can be elicited via mere observation, suggestive of shared processing across sensory modalities. Finally, upon establishing facial EMG as a suitable methodology to study affective reactions to affective touch, researchers can then explore how affective touch processing may be influenced by various interventions (e.g., drug administration; stress exposure), how it changes throughout development7, how it is influenced by the relationship of the interactants33, and whether it is dysregulated in clinical populations8.

<table><tbody><tr><td>4 mm Ag-AgCl sheilded reusable electrodes</td><td>Biopac</td><td>EL654</td><td></td></tr><tr><td>75 mm goat hair brush</td><td>IN-EX Color AB</td><td>77062</td><td>Touch application; https://www.in-exfarg.se</td></tr><tr><td>8 mm Ag-AgCl unsheilded reusable electrode</td><td>Biopac</td><td></td><td></td></tr><tr><td>Acqknowledge software</td><td>Biopac</td><td>ACK100W</td><td>Used for application of filtering steps, analysis</td></tr><tr><td>Adhesive collars</td><td>Biopac</td><td>ADD204</td><td></td></tr><tr><td>Cables</td><td>Biopac</td><td>BN-EL30-LEAD3; LEAD2</td><td>LEAD3 includes ground, LEAD2 is only bipolar recording electrodes</td></tr><tr><td>Electro-gel</td><td>Biopac</td><td>GEL100</td><td></td></tr><tr><td>EMG aplifier x 2</td><td>Biopac</td><td>BN-EMG2</td><td></td></tr><tr><td>El-Prep</td><td>Biopac</td><td>ELPREP</td><td>Facial exfoliant</td></tr><tr><td>MP160 data acqusition system</td><td>Biopac</td><td>MP160WSW</td><td></td></tr><tr><td>Presentation software</td><td>Neurobehavioral systems</td><td></td><td>Task presentation software</td></tr></tbody></table>

using facial electromyography to assess facial muscle reactions to experienced and observed affective touch in humans

"Affective" touch is believed to be processed in a manner distinct from discriminatory touch and to involve activation of C-tactile (CT) afferent fibers. Touch that optimally activates CT fibers is consistently rated as hedonically pleasant. Patient groups with impaired social-emotional functioning also show disordered affective touch ratings. However, relying on self-reported ratings of touch has many limitations, including recall bias and communication barriers. Here, we describe a methodological approach to study affective responses to touch via facial electromyography (EMG) that circumvents the reliance on self-report ratings. Facial EMG is an objective, quantitative, and non-invasive method to measure facial muscle activity indicative of affective responses. Responses can be assessed across healthy and patient populations without the need for verbal communication. Here, we provide two separate datasets demonstrating that CT-optimal and non-optimal touch elicit distinct facial muscle reactions. Moreover, facial EMG responses are consistent across stimulus modalities, e.g. tactile (experienced touch) and visual (observed touch). Finally, the temporal resolution of facial EMG can detect responses on timescales that supersede that of verbal reporting. Together, our data suggest that facial EMG is a suitable methodology for use in affective tactile research that can be used to supplement, or in some cases, supplant, existing measures.

CT-optimal touch elicits distinct EMG responses compared to fast non-optimal touch across modalities 
The first experiment addressed whether differential EMG reactivity could be detected in response to CT-optimal (3 cm/s) and fast non-optimal (30 cm/s) tactile stimulation that was directly experienced (Figure 3) or merely observed (Figure 2 and Figure 3)31.
Experienced CT-optimal touch was rated as more pleasant than non-optimal touch (F(1,28) = 32.2; p &#60; 0.001; Figure 3A) regardless of touch location (p = 0.063; velocity x location: p = 0.32). Similarly, observed CT-optimal touch was rated as more pleasant that non-optimal touch (touch velocity: F(1,28) = 47.5; p &#60; 0.001; touch type: F(2,56) = 6.09, p = 0.004; type x velocity interaction F(2,56) = 5.87, p = 0.005). CT-optimal touch to the arm was rated as more pleasant than touch to the palm (p = 0.024) and non-social touch (e.g., touch to the wooden arm; p = 0.001). Fast non-optimal touch was always rated as more intense (Figure 3B), regardless of whether the touch was experienced (touch velocity: F(1,28) = 34.3, p &#60; 0.001; touch location: p = 0.28; velocity x location interaction: p = 0.64) or observed (touch velocity: F(1,28) = 35.1, p &#60; 0.001; touch type: p = 0.40; velocity x type interaction: p = 0.39).
Experienced fast, non-optimal touch elicited robust corrugator reactivity that was mitigated by recruitment of CT-afferents during CT-optimal touch (effect of touch velocity: F(1,28) = 4.84, p = 0.036; effect of touch location: p = 0.93; touch velocity x location interaction: p = 0.42; Figure 3C). Corrugator response significantly differed between CT-optimal and non-optimal touch for touch to the arm (p = 0.050) but only trend level effects were seen for touch to the palm (p = 0.092). There was no main effect of touch velocity (p = 0.11) or type (p = 0.79) on corrugator reactivity to observed touch, but there was a touch velocity x type interaction (F(2,56) = 3.80, p = 0.028). Post hoc tests revealed that fast non-optimal touch elicited greater corrugator reactivity than CT-optimal touch particularly for videos of touch to the arm (p = 0.007), but not touch to the palm (p = 0.13) or non-social touch (p = 0.25). Zygomatic activity was not significantly affected by experienced touch (effect of touch velocity: p = 0.15; effect of touch type: p = 0.73; touch velocity x type interaction: p = 0.63; Figure 3D), nor observed touch (main effect of touch velocity: p = 0.37; main effect of touch type: p = 0.84; touch velocity x type interaction: p = 0.23).
CT-optimal touch elicits EMG responses distinct from slow non-optimal touch 
Experiment 2 assessed whether slow non-optimal (0.3 cm/s) would elicit similar responses as fast non-optimal (30 cm/s)32. We found that slow non-optimal touch was rated as less pleasant (Figure 4A) and less intense (Figure 4B) than CT-optimal touch. Similar to fast non-optimal touch, slow non-optimal touch elicited robust corrugator activitythat was attenuated by CT-optimal touch (effect of touch velocity: F(1,83) = 9.723, p = 0.002; Figure 4C). There was no effect of touch on zygomatic activity (p&#160;= 0.35; Figure 4D).
We next assessed the time course of EMG responses. During the first 700 ms, a window putatively free of CT input, there was no difference in corrugator reactivity (-0.031 &#177; 0.06 &#181;V and -0.017 &#177; 0.49 &#181;V, pBon = 0.98; Figure 5A). However, over the next 5.6 s, corrugator reactivity in response to CT optimal touch decreased gradually, whereas it gradually increased in response to slow non-optimal touch: during interval 2, the corrugator reactivity was marginally lower for CT optimal touch than non-optimal touch (pBon = 0.071). During intervals 3, 5, 6, 7 and 8, the corrugator reactivity was significantly lower during CT optimal touch than during non-optimal touch (pBon &#60; 0.034; Figure 5A). This pattern was absent in analysis of zygomatic reactivity (p = 0.83; Figure 5B).
<img alt="Figure 1" class="xfigimg" src="/files/ftp_upload/59228/59228fig1.jpg" /> 
Figure 1: Example of experimental setup for the Experienced Touch task. Seat the participant in front of the computer with their arm extended laterally, comfortably resting on a cushion. If obtaining self-report ratings, it is recommended to apply touch to the arm that is not used to provide ratings to avoid potential movement artifacts from contaminating the EMG signal. The arm should be occluded from view of the participant35,39, either with customized glasses, as above, or using a curtain separator. This figure is adapted from Ree et al.32 <a href="https://www-jove-com.bdigitaluss.remotexs.co/files/ftp_upload/59228/59228fig1large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 2" class="xfigimg" src="/files/ftp_upload/59228/59228fig2.jpg" /> 
Figure 2: Example of touch stimuli used in the Observed Touch task. The observed touch task included 6 s videos of touch to the (A) CT-rich arm, (B) CT-lacking palm, and (C) non-social touch to a wooden arm. <a href="https://www-jove-com.bdigitaluss.remotexs.co/files/ftp_upload/59228/59228fig2large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 3" class="xfigimg" src="/files/ftp_upload/59228/59228fig3.jpg" /> 
Figure 3: CT-optimal touch elicits distinct responses compared to fast non-optimal touch across modalities. (A) CT-optimal touch (3 cm/s) is consistently rated as more pleasant than fast non-optimal touch (30 cm/s) across both tasks. Experienced touch is rated as most pleasant, followed by social (arm, palm) observed touch, then non-social touch (e.g. touch to a wooden arm). (B) CT-optimal touch is (3 cm/s) rated as less intense across modalities, regardless of modality or social content. (C) Fast non-optimal touch (30 cm/s) elicits more corrugator reactivity than CT-optimal touch (3 cm/s). This difference is most robust for touch to the CT-rich arm. (D) CT-optimal touch (3 cm/s) marginally increases zygomatic reactivity, though this does not reach significance for any modality or location. Bars and errors bars represent mean and standard error of the mean; *p &#60; 0.05 effect of velocity. This figure is adapted from Mayo et al.31 <a href="https://www-jove-com.bdigitaluss.remotexs.co/files/ftp_upload/59228/59228fig3large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 4" class="xfigimg" src="/files/ftp_upload/59228/59228fig4.jpg" /> 
Figure 4: CT-optimal touch (3 cm/s) elicits distinct responses as compared to slow non-optimal touch (0.3 cm/s). (A) CT-optimal touch (3 cm/s) is rated as more pleasant than slow non-optimal touch (0.3 cm/s). (B) CT-optimal touch (3 cm/s) is rated as more intense than slow non-optimal touch (0.3 cm/s). (C) Mean corrugator reactivity in response to CT-optimal (3 cm/s) is reduced compared to slow non-optimal (0.3 cm/s). (D) Touch does not significantly influence zygomatic reactivity. Bars and error bars represent means and standard error of the mean; *p &#60; 0.05. This figure is adapted from Ree et al.32 <a href="https://www-jove-com.bdigitaluss.remotexs.co/files/ftp_upload/59228/59228fig4large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 5" class="xfigimg" src="/files/ftp_upload/59228/59228fig5.jpg" /> 
Figure 5: Corrugator responses to CT-optimal touch are temporally specific. (A) When binned in intervals of 700 ms, CT-optimal touch elicits significantly less corrugator reactivity. The exception is in the first 700 ms, which is putatively free of CT input due to the slower conduction velocity of these unmyelinated afferents. (B) Zygomatic reactivity is not significantly different in response to optimal or slow non-optimal touch at any of the time points. Dots represent means and bars represent standard errors of the mean. This figure is adapted from Ree et al.32 <a href="https://www-jove-com.bdigitaluss.remotexs.co/files/ftp_upload/59228/59228fig5large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Table 1" src="/files/ftp_upload/59228/59228table1.jpg" /> 
Table 1: Summary of Experimental Designs. In the Experienced Touch task of Experiment 1, touch was delivered at CT-optimal (3 cm/s) or fast non-optimal (30 cm/s) velocities to hairy (arm) and glabrous (palm) skin. The Observed Touch instead included videos of touch delivered to the arm, palm, or to a wooden arm (e.g., non-social) at the same touch velocities. The &#34;non-social&#34; condition was included to control for potential responses elicited by low-level periodicity information encoded in the movement17, and determine the relevance of social content38 on ratings and EMG responses. Results were analyzed using repeated measures analysis of variance (ANOVA) with touch velocity and touch type as within-subjects factors. A post-hoc power analysis based on Experiment 1 suggests at least 22 individuals should be included to achieve similar effects. In Experiment 2, touch was delivered to the arm at CT-optimal (3 cm/s) or slow non-optimal (0.3 cm/s) velocities. Touch was delivered for a total of 2min, but here we only report on the first 6.3 s in order to compare results to Experiment 1. Each velocity was repeated twice. In all experiments, self-reported ratings of the affective quality (e.g., pleasantness) and discriminative aspects (e.g., intensity) were assessed10.

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Using Facial Electromyography to Assess Facial Muscle Reactions to Experienced and Observed Affective Touch in Humans

We describe a protocol to assess facial muscle activity in response to experienced and observed tactile stimulation using facial electromyography.

"Affective" touch is believed to be processed in a manner distinct from discriminatory touch and to involve activation of C-tactile (CT) afferent fibers. ...

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11.0K Views.  University of Oslo. We describe a protocol to assess facial muscle activity in response to experienced and observed tactile stimulation using facial electromyography.

Video: Using Facial Electromyography to Assess Facial Muscle Reactions to Experienced and Observed Affective Touch in Humans

Corticospinal Excitability Modulation During Action Observation

This study used the transcranial magnetic stimulation/motor evoked potential (TMS/MEP) technique to pinpoint when the automatic tendency to mirror someone else's action becomes anticipatory simulation of a complementary act. TMS was delivered to the left primary motor cortex corresponding to the hand to induce the highest level of MEP activity from the abductor digiti minimi (ADM; the muscle serving little finger abduction) as well as the first dorsal interosseus (FDI; the muscle serving index finger flexion/extension) muscles. A neuronavigation system was used to maintain the position of the TMS coil, and electromyographic (EMG) activity was recorded from the right ADM and FDI muscles. Producing original data with regard to motor resonance, the combined TMS/MEP technique has taken research on the perception-action coupling mechanism a step further. Specifically, it has answered the questions of how and when observing another person's actions produces motor facilitation in an onlooker's corresponding muscles and in what way corticospinal excitability is modulated in social contexts.

Single-pulse transcranial magnetic stimulation over the primary motor cortex, neuronavigation, and registration of electromyographic activity of hand muscles were used in this study to explore corticospinal excitability while participants were observing action sequences.

This study used the transcranial magnetic stimulation/motor evoked potential (TMS/MEP) technique to pinpoint when the automatic tendency to mirror someone ...

Force and Position Control in Humans - The Role of Augmented Feedback

During motor behaviour, humans interact with the environment by for example manipulating objects and this is only possible because sensory feedback is constantly integrated into the central nervous system and these sensory inputs need to be weighted in order meet the task specific goals. Additional feedback presented as augmented feedback was shown to have an impact on motor control and motor learning. A number of studies investigated whether force or position feedback has an influence on motor control and neural activation. However, as in the previous studies the presentation of the force and position feedback was always identical, a recent study assessed whether not only the content but also the interpretation of the feedback has an influence on the time to fatigue of a sustained submaximal contraction and the (inhibitory) activity of the primary motor cortex using subthreshold transcranial magnetic stimulation. This paper describes one possible way to investigate the influence of the interpretation of feedback on motor behaviour by investigating the time to fatigue of submaximal sustained contractions together with the neuromuscular adaptations that can be investigated using surface EMG. Furthermore, the current protocol also describes how motor cortical (inhibitory) activity can be investigated using subthreshold TMS, a method known to act solely on the cortical level. The results show that when participants interpret the feedback as position feedback, they display a significantly shorter time to fatigue of a submaximal sustained contraction. Furthermore, subjects also displayed an increased inhibitory activity of the primary cortex when they believed to receive position feedback compared when they believed to receive force feedback. Accordingly, the results show that interpretation of feedback results in differences on a behavioural level (time to fatigue) that is also reflected in interpretation-specific differences in the amount of inhibitory M1 activity.

Controlling an identical movement with position or force feedback results in different neural activation and motor behavior. This protocol describes how to investigate behavioral changes by looking at neuromuscular fatigue and how to evaluate motor cortical (inhibitory) activity using subthreshold TMS with respect to the interpretation of augmented feedback.

During motor behaviour, humans interact with the environment by for example manipulating objects and this is only possible because sensory feedback is ...

Testing Tactile Masking between the Forearms

Masking, in which one stimulus affects the detection of another, is a classic technique that has been used in visual, auditory, and tactile research, usually using stimuli that are close together to reveal local interactions. Masking effects have also been demonstrated in which a tactile stimulus alters the perception of a touch at a distant location. Such effects can provide insight into how components of the body's representations in the brain may be linked. Occasional reports have indicated that touches on one hand or forearm can affect tactile sensitivity at corresponding contralateral locations. To explore the matching of corresponding points across the body, we can measure the spatial tuning and effect of posture on contralateral masking. Careful controls are required to rule out direct effects of the remote stimulus, for example by mechanical transmission, and also attention effects in which thresholds may be altered by the participant's attention being drawn away from the stimulus of interest. The use of this technique is beneficial as a behavioural measure for exploring which parts of the body are functionally connected and whether the two sides of the body interact in a somatotopic representation. This manuscript describes a behavioural protocol that can be used for studying contralateral tactile masking.

Here we explore contralateral tactile masking between the forearms in which tactile detection thresholds are modulated by vibration applied to a remote site. The details of which remote sites have an effect can tell us about how the body is represented in the brain.

Masking, in which one stimulus affects the detection of another, is a classic technique that has been used in visual, auditory, and tactile research, ...

Conscious and Non-conscious Representations of Emotional Faces in Asperger's Syndrome

Several neuroimaging studies have suggested that the low spatial frequency content in an emotional face mainly activates the amygdala, pulvinar, and superior colliculus especially with fearful faces1-3. These regions constitute the limbic structure in non-conscious perception of emotions and modulate cortical activity either directly or indirectly2. In contrast, the conscious representation of emotions is more pronounced in the anterior cingulate, prefrontal cortex, and somatosensory cortex for directing voluntary attention to details in faces3,4. Asperger&#39;s syndrome (AS)5,6 represents an atypical mental disturbance that affects sensory, affective and communicative abilities, without interfering with normal linguistic skills and intellectual ability. Several studies have found that functional deficits in the neural circuitry important for facial emotion recognition can partly explain social communication failure in patients with AS7-9. In order to clarify the interplay between conscious and non-conscious representations of emotional faces in AS, an EEG experimental protocol is designed with two tasks involving emotionality evaluation of either photograph or line-drawing faces. A pilot study is introduced for selecting face stimuli that minimize the differences in reaction times and scores assigned to facial emotions between the pretested patients with AS and IQ/gender-matched healthy controls. Information from the pretested patients was used to develop the scoring system used for the emotionality evaluation. Research into facial emotions and visual stimuli with different spatial frequency contents has reached discrepant findings depending on the demographic characteristics of participants and task demands2. The experimental protocol is intended to clarify deficits in patients with AS in processing emotional faces when compared with healthy controls by controlling for factors unrelated to recognition of facial emotions, such as task difficulty, IQ and gender.

An EEG experimental protocol is designed to clarify the interplay between conscious and non-conscious representations of emotional faces in patients with Asperger&#39;s syndrome. The technique suggests that patients with Asperger&#39;s syndrome have deficits in non-conscious representation of emotional faces, but have comparable performance in conscious representation with healthy controls.

Several neuroimaging studies have suggested that the low spatial frequency content in an emotional face mainly activates the amygdala, pulvinar, and ...

Protocol for Data Collection and Analysis Applied to Automated Facial Expression Analysis Technology and Temporal Analysis for Sensory Evaluation

We demonstrate a method for capturing emotional response to beverages and liquefied foods in a sensory evaluation laboratory using automated facial expression analysis (AFEA) software. Additionally, we demonstrate a method for extracting relevant emotional data output and plotting the emotional response of a population over a specified time frame. By time pairing each participant's treatment response to a control stimulus (baseline), the overall emotional response over time and across multiple participants can be quantified. AFEA is a prospective analytical tool for assessing unbiased response to food and beverages. At present, most research has mainly focused on beverages. Methodologies and analyses have not yet been standardized for the application of AFEA to beverages and foods; however, a consistent standard methodology is needed. Optimizing video capture procedures and resulting video quality aids in a successful collection of emotional response to foods. Furthermore, the methodology of data analysis is novel for extracting the pertinent data relevant to the emotional response. The combinations of video capture optimization and data analysis will aid in standardizing the protocol for automated facial expression analysis and interpretation of emotional response data.

A protocol for capturing and statistically analyzing emotional response of a population to beverages and liquefied foods in a sensory evaluation laboratory using automated facial expression analysis software is described.

We demonstrate a method for capturing emotional response to beverages and liquefied foods in a sensory evaluation laboratory using automated facial ...

Somatosensory Event-related Potentials from Orofacial Skin Stretch Stimulation

Cortical processing associated with orofacial somatosensory function in speech has received limited experimental attention due to the difficulty of providing precise and controlled stimulation. This article introduces a technique for recording somatosensory event-related potentials (ERP) that uses a novel mechanical stimulation method involving skin deformation using a robotic device. Controlled deformation of the facial skin is used to modulate kinesthetic inputs through excitation of cutaneous mechanoreceptors. By combining somatosensory stimulation with electroencephalographic recording, somatosensory evoked responses can be successfully measured at the level of the cortex. Somatosensory stimulation can be combined with the stimulation of other sensory modalities to assess multisensory interactions. For speech, orofacial stimulation is combined with speech sound stimulation to assess the contribution of multi-sensory processing including the effects of timing differences. The ability to precisely control orofacial somatosensory stimulation during speech perception and speech production with ERP recording is an important tool that provides new insight into the neural organization and neural representations for speech.

This paper introduces a method for obtaining somatosensory event-related potentials following orofacial skin stretch stimulation. The current method can be used to evaluate the contribution of somatosensory afferents to both speech production and speech perception.

Cortical processing associated with orofacial somatosensory function in speech has received limited experimental attention due to the difficulty of ...

Neuroscience

An Emerging Target Paradigm to Evoke Fast Visuomotor Responses on Human Upper Limb Muscles

To reach towards a seen object, visual information has to be transformed into motor commands. Visual information such as the object&#8217;s color, shape, and size are processed and integrated within numerous brain areas, then ultimately relayed to the motor periphery. In some instances, a reaction is needed as fast as possible. These fast visuomotor transformations, and their underlying neurological substrates, are poorly understood in humans as they have lacked a reliable biomarker. Stimulus-locked responses (SLRs) are short latency (&#60;100 ms) bursts of electromyographic (EMG) activity representing the first wave of muscle recruitment influenced by visual stimulus presentation. SLRs provide a quantifiable output of rapid visuomotor transformations, but SLRs have not been consistently observed in all subjects in past studies. Here we describe a new, behavioral paradigm featuring the sudden emergence of a moving target below an obstacle that consistently evokes robust SLRs. Human participants generated visually guided reaches toward or away from the emerging target using a robotic manipulandum while surface electrodes recorded EMG activity from the pectoralis major muscle. In comparison to previous studies that investigated SLRs using static stimuli, the SLRs evoked with this emerging target paradigm were larger, evolved earlier, and were present in all participants. Reach reaction times (RTs) were also expedited in the emerging target paradigm. This paradigm affords numerous opportunities for modification that could permit systematic study of the impact of various sensory, cognitive, and motor manipulations on fast visuomotor responses. Overall, our results demonstrate that an emerging target paradigm is capable of consistently and robustly evoking activity within a fast visuomotor system.

Presented here is a behavioral paradigm that elicits robust fast visuomotor responses on human upper limb muscles during visually guided reaches.

To reach towards a seen object, visual information has to be transformed into motor commands. Visual information such as the object&#8217;s color, shape, ...

Capturing Dynamic Finger Gesturing with High-resolution Surface Electromyography and Computer Vision

Finger gestures are a critical element in human communication, and as such, finger gesture recognition is widely studied as a human-computer interface for state-of-the-art prosthetics and optimized rehabilitation. Surface electromyography (sEMG), in conjunction with deep learning methods, is considered a promising method in this domain. However, current methods often rely on cumbersome recording setups and the identification of static hand positions, limiting their effectiveness in real-world applications. The protocol we report here presents an advanced approach combining a wearable surface EMG and finger&#160;tracking system to capture comprehensive data during dynamic hand movements. The method records muscle activity from soft printed electrode arrays (16 electrodes) placed on the forearm as subjects perform gestures in different hand positions and during movement. Visual instructions prompt subjects to perform specific gestures while EMG and finger positions are recorded. The integration of synchronized EMG recordings and finger tracking data enables comprehensive analysis of muscle activity patterns and corresponding gestures. The reported approach demonstrates the potential of combining EMG and visual tracking technologies as an important resource for developing intuitive and responsive gesture recognition systems with applications in prosthetics, rehabilitation, and interactive technologies. This protocol aims to guide researchers and practitioners, fostering further innovation and application of gesture recognition in dynamic and real-world scenarios.

The paper presents a comprehensive protocol for simultaneously recording hand-electromyography (EMG) and visual finger tracking during natural finger gesturing. The visual data is designed to serve as the ground truth for the development of accurate EMG-based computational models for finger gesture recognition.

Finger gestures are a critical element in human communication, and as such, finger gesture recognition is widely studied as a human-computer interface for ...

Bioengineering

Assessing Corticospinal Excitability During Goal-Directed Reaching Behavior

Reaching is a widely studied behavior in motor physiology and neuroscience research. While reaching has been examined using a variety of behavioral manipulations, there remain significant gaps in the understanding of the neural processes involved in reach planning, execution, and control. The novel approach described here combines a two-dimensional reaching task with transcranial magnetic stimulation (TMS) and concurrent electromyography (EMG) recording from multiple muscles. This method allows for the noninvasive detection of corticospinal activity at precise time points during the unfolding of reaching movements. The example task code includes a delayed response reaching task with two possible targets displayed &#177; 45&#176; off the midline. Single pulse TMS is delivered on the majority of task trials, either at the onset of the preparatory cue (baseline) or 100 ms prior to the imperative cue (delay). This sample design is suitable for investigating changes in corticospinal excitability during reach preparation. The sample code also includes a visuomotor perturbation (i.e., cursor rotation of &#177; 20&#176;) to investigate the effects of adaptation on corticospinal excitability during reach preparation. The task parameters and TMS delivery can be adjusted to address specific hypotheses about the state of the motor system during reaching behavior. In the initial implementation, motor evoked potentials (MEPs) were successfully elicited on 83% of TMS trials, and reach trajectories were recorded on all trials.

Reaching is a fundamental skill that allows humans to interact with the environment. Several studies have aimed to characterize reaching behavior using a variety of methodologies. This paper offers an open-source application of transcranial magnetic stimulation to assess the state of corticospinal excitability in humans during reaching task performance.

Reaching is a widely studied behavior in motor physiology and neuroscience research. While reaching has been examined using a variety of behavioral ...

Using TMS to Measure Motor Excitability During Action Observation

Source: Laboratories of Jonas T. Kaplan and Sarah I. Gimbel&#8212;University of Southern California
Transcranial Magnetic Stimulation (TMS) is a non-invasive brain stimulation technique that involves passing current through an insulated coil placed against the scalp. A brief magnetic field is created by current in the coil, and because of the physical process of induction, this leads to a current in the nearby neural tissue. Depending on the duration, frequency, and magnitude of these magnetic pulses, the underlying neural circuitry can be affected in many different ways. Here, we demonstrate the technique of single-pulse TMS, in which one brief magnetic pulse is used to stimulate the neocortex.
One observable effect of TMS is that it can produce muscle twitches when applied over the motor cortex. Due to the somatotopic organization of the motor cortex, different muscles can be targeted depending on the precise placement of the coil. The electrical signals that cause these muscle twitches, called motor evoked potentials, or MEPs, can be recorded and quantified by electrodes placed on the skin over the targeted muscle. The amplitude of MEPs can be interpreted to reflect the underlying excitability of the motor cortex; for example, when the motor cortex is activated, observed MEPs are larger. 
In this experiment, based on a study originally performed by Fadiga and colleagues1 and since replicated by many others,2 we use single-pulse TMS to test the excitability of motor cortex during action observation. It is known that motor cortex can be activated not only when we move, but when we watch others perform movements. A common interpretation of this phenomenon is that it reflects a simulation process that may play a role in the understanding of others' actions. Here we will record MEPs evoked by TMS over the primary motor cortex while subjects observe the movements of others compared with control stimuli.