The authors would like to thank Verena Friedrich, Maria Velana, Sandra Gebhardt, Romy B&#228;rwaldt, and Tina Daucher for their precious help in conducting the study. In addition, a special thank you goes out to Dr. Stefanie Rukavina for her scientific support. This research was part of the DFG/TR233/12 (http://www.dfg.de/) &#8220;Advancement and Systematic Validation of an Automated Pain Recognition System on the Basis of Facial Expression and Psychobiological Parameters&#8221; project, funded by the German Research Foundation.

<ol>
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A., Mian, S., Kendzerska, T., French, M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Measures+of+adult+pain:+Visual+analog+scale+for+pain+(vas+pain),+numeric+rating+scale+for+pain+(nrs+pain),+mcgill+pain+questionnaire+(mpq),+short&#8208;form+mcgill+pain+questionnaire+(sf&#8208;mpq),+chronic+pain+grade+scale+(cpgs),+short+form&#8208;36+bodily+pain+scale+(sf&#8208;36+bps),+and+measure+of+intermittent+and+constant+osteoarthritis+pain+(icoap).">Measures of adult pain: Visual analog scale for pain (vas pain), numeric rating scale for pain (nrs pain), mcgill pain questionnaire (mpq), short&#8208;form mcgill pain questionnaire (sf&#8208;mpq), chronic pain grade scale (cpgs), short form&#8208;36 bodily pain scale (sf&#8208;36 bps), and measure of intermittent and constant osteoarthritis pain (icoap).</a> Arthritis Care &amp; Research. 63 (11), 240-252 (2011).</li><li>Kehlet, H. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Acute+pain+control+and+accelerated+postoperative+surgical+recovery.">Acute pain control and accelerated postoperative surgical recovery.</a> Surgical Clinics. 79, 431-443 (1999).</li><li>McQuay, H., Moore, A., Justins, D. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Treating+acute+pain+in+hospital.">Treating acute pain in hospital.</a> British Medical Journal. 314 (7093), 1531-1535 (1997).</li><li>Brahnam, S., Chuang, C. 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The authors have nothing to disclose.

The presented protocol focuses on the experimental elicitation of thermal (heat) and electrical pain while recording physiological, visual and paralinguistic signals. This novel approach, combining two pain models with different stimuli intensities and two different stimuli durations (phasic and tonic), offers a broad perspective about the psychophysiological patterns and expressions of pain. However, for the realization of this protocol several steps need to be considered.&#160;
In general, if working with pain stimuli it is crucial to ensure the safety of the subjects. All pain stimuli have to be highly controlled and should only be carried out by experienced experimenters.&#160; 
Furthermore, for recording and collecting reliable and high quality data, the proper attachment of devices (electrodes), the perfect functioning of recording devices and a smooth communication between computers is highly recommended. All sources of interferences should be eliminated or reduced to a minimum. To guarantee consistency between participants, it is important to provide standardized instructions and unvarying experimental conditions.&#160;
According to our experience, finding suitable participants who meet all criteria and are willing to receive numerous painful stimuli, takes a long time and is quite challenging. In addition to that, the monetary compensation has to be high enough to attract subjects to the study. Especially persons between 30 and 50 years are hard to find. This may be because the experiment is too long (ca. 4 hours, including arrival and departure) and they have to take half a day off from work.
Because the safety of the participants is of top priority, pain induction may need to be restricted. Due to ethical guidelines, the stimulus intensities must not exceed certain levels to prevent burns and unconsciousness in terms of thermal and electrical pain induction, respectively. A general cutoff of intensities may result in a ceiling effect as some subjects may reach the intensity limits before feeling intolerable pain. In this study, approximately 42% (considering&#160;thermal calibration part 1 and 2) of the participants reached the thermal cutoffs (see representative results). As they did not reach their &#8220;real&#8221; pain tolerances, their physiological responses to the highest thermal stimuli might behave differently in contrast to physiological responses of subjects who reached them. If so, mixing these two groups could influence classification results in terms of pain recognition.
An important point to address is the pain modalities in this experiment. Participants are subjected only to thermal and electrical pain stimuli (due to the fact that these are highly controllable in an experimental setting). Thus, if examining pain patterns regarding quality, findings may not translate to other pain modalities such as pressure, chemical or visceral pain. 
The same consideration on transferability of results applies to the study sample. The protocol is ethically restricted to healthy adults. For example, it does not include children or cognitively and verbally impaired persons. Furthermore, in our study only European people participated. Also here, analytical results may not apply to groups not considered in this experiment.
Another limitation may concern the Hawthorne effect24: The subjects are aware that they are being filmed/observed in the study. This might change their behavior.
Compared to existing pain databases, the protocol provides significant advantages for analyzing pain response patterns as it combines two pain models and two time courses (phasic and tonic): Besides the intensity and duration of pain, it also considers the quality of pain. As thermal pain is described differently than electrical pain (e.g., burning vs. sharp), it may also differ in the pain reactions. If so, those findings could link a pain response pattern to the underlying source of pain. Furthermore, the study is multi-modal to widen the range of pain investigation opportunities: Employing 5 psychophysiological signals, 2 face (front/side) camera signals, 1 body view camera signal, 1 thermal camera and 1 audio signal, pain may be analyzed and assessed more precisely.&#160;
For a more complex investigation of pain response patterns, future extensions of this method should include more biosignals such as electroencephalography (EEG), body temperature and respiration. It would also be of great benefit to employ controlled pressure as a further pain model. Researchers aiming at automatic pain recognition via data gathered with this protocol should further test promising machine learning models with clinical control groups.&#160;

Pain is a very personal and unpleasant sensation that is perceived differently by everyone. It lasts from seconds to months and may vary in its quality (throbbing, sharp, burning, etc.). If treated inadequately, pain influences physical and psychological functions of the body, reduces the quality of life, and bears the risk of becoming a chronic condition. In clinical care, the accurate assessment of pain intensity and quality is highly relevant to provide successful pain management1,2. Gold standard methods for assessing pain, such as the visual analog scales (VAS), the numeric rating scale (NRS), or the McGill Pain Questionnaire3, rely on self-reports of patients and, thus, only work sufficiently with cognitively and verbally unimpaired persons. Consequently, all those established methods lack validity and reliability when it comes to neonates4, delirious, somnolent, sedated, or ventilated patients5, or people suffering from dementia6,7. In addition to or as an alternative to self-report scales, methods to measure pain through observation by trained personnel (e.g., the Zurich Observation Pain Assessment8 or the Abbey Pain Scale9) have been developed in recent years. Nevertheless, even these tools suffer from limitations in reliability and validity, as even trained raters cannot guarantee an objective assessment. Furthermore, the application is often too time-consuming for clinical staff when pain assessment should be done on a regular basis.&#160;
Several research teams have focused on developing automated pain recognizing systems, which allow for measuring pain by means of physiological, visual, and/or paralinguistic signal sets as new approaches for assessing and monitoring pain and its intensities objectively. Previous studies show promising results in detecting and differentiating pain10,11,12,13,16,17,18 or discriminating pain from basic emotions14,15 based solely on one of the signal sets10,11,12,13,14,15 as well as on a combination/fusion16,17,19 of the sets. The abovementioned modalities react almost autonomously to stressful stimuli such as pain. Using them has the advantage that they do not require a person&#8217;s ability to report her/his pain. Such individuals would greatly benefit from an objective pain recognition system which incorporates such modalities. Data sets consisting of reactions to elicited pain provide precious information for analyzing pain patterns and developing practical applications for detecting and monitoring pain. Amongst others, Walter et al.20 created the &#8220;BioVid Heat Pain Database&#8221;, a multimodal database that is publicly available and provides data from short-time induced painful heat stimuli and corresponding psychophysiological and visual reactions. The &#8220;SenseEmotion Database&#8221; of Velana et al.21 includes biosignals, videos, and paralinguistic information from volunteers affected by phasic heat pain and emotional stimuli.
While these databases are well suited for examining pain reactions, they are mostly based on one specific pain model. As pain differs in its quality (supposedly depending on the pain model) and in its duration, it also may differ in its physiological, visual, and paralinguistic correlates. To the best of the authors&#8217; knowledge, no multimodal studies or databases exist that combine two or more pain models and vary pain stimuli in intensity and duration in order to not only detect pain patterns but also distinguish between pain qualities.
This paper provides a protocol on how to conduct a complex psychophysiological experiment to elicit pain and simultaneously record physiological responses (e.g., ECG, EMG of Musculus trapezius, corrugator supercilii, and zygomaticus major, SCL) as well as video (e.g., facial expressions, body gestures, facial skin temperature) and audio data. Participants are stimulated with short (phasic) and longer lasting (tonic) heat and electrical pain stimuli that differ in intensity. A calibration phase prior to the experiment determines pain thresholds for each subject individually.
The study aims at collecting multimodal data for investigating pain (patterns) regarding intensity, quality and length by means of statistical methods, machine learning algorithms, etc. Additionally, the already collected data is planned to be published for academic research purposes under the name &#8220;X-ITE (Experimentally Induced Thermal and Electrical) Pain Database&#8221;. It may extend existing databases, such as BioVid Heat Pain and SenseEmotion20,21, and contribute to the further development, improvement, and/or evaluation of automated pain recognition systems in matters of validity, reliability, and real-time recognition.&#160;
The rest of the paper is organized in the following way. The protocol describes how to carry out the pain elicitation study step-by-step. Then, the representative results present the outcome of the experiment. Finally, the discussion covers critical steps, limitations, and benefits of the study followed by suggestions for future extensions.

<table border="0" cellpadding="0" cellspacing="0" style="width:1531px;" width="1532">
	<tbody>
		<tr height="21">
			<td height="21" style="height:21px;width:419px;">PATHWAY Model ATS</td>
			<td style="width:514px;">Medoc Ltd., Ramat Yishai, Israel</td>
			<td style="width:178px;"></td>
			<td style="width:422px;">Thermal Stimulator</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:419px;">30 mm x 30 mm ATS Thermode</td>
			<td style="width:514px;">Medoc Ltd., Ramat Yishai, Israel</td>
			<td style="width:178px;"></td>
			<td style="width:422px;">Thermode</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:419px;">PATHWAY Software Arbel 6.3.7.22.1</td>
			<td style="width:514px;">Medoc Ltd., Ramat Yishai, Israel</td>
			<td style="width:178px;"></td>
			<td>Thermal Stimulator Software</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:419px;">Digitimer DS7A Current Stimulator</td>
			<td style="width:514px;">Digitimer Ltd., Hertfordshire, UK</td>
			<td style="width:178px;"></td>
			<td style="width:422px;">Electrical Stimulator</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:419px;">Inquisit 5</td>
			<td style="width:514px;">Millisecond Software, Seattle, WA, USA</td>
			<td style="width:178px;"></td>
			<td style="width:422px;">Software for triggering electrical stimuli</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:419px;">Analogue-To-Digital Converter</td>
			<td style="width:514px;">Wissenschaftliche Werkstatt Elektronik, University of Ulm, Ulm, Germany</td>
			<td style="width:178px;">custom built</td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:419px;">BIOPAC MP150 System</td>
			<td style="width:514px;">BIOPAC Systems, Inc., Goleta, CA, USA</td>
			<td style="width:178px;"></td>
			<td>Biosignal Recording Hardware</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;">AcqKnowledge Software 4.1.1</td>
			<td style="width:514px;">BIOPAC Systems, Inc., Goleta, CA, USA</td>
			<td style="width:178px;"></td>
			<td>Biosignal Recording Software</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:419px;">NTG-2 Dual Powered Directional Condenser Microphone</td>
			<td style="width:514px;">R&#216;DE Microphones, Silverwater, Australia</td>
			<td style="width:178px;"></td>
			<td>Audio Recording Microphone</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:419px;">Kinect v2</td>
			<td style="width:514px;">Microsoft, Redmond, WA, USA</td>
			<td style="width:178px;"></td>
			<td>Body View Camera</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:419px;">AV Pike F-145C</td>
			<td style="width:514px;">Allied Vision Technologies GmbH, Stadtroda, Germany</td>
			<td style="width:178px;"></td>
			<td>Face Camera (frontal view)</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:419px;">AV Prosilica GT 1600C</td>
			<td style="width:514px;">Allied Vision Technologies GmbH, Stadtroda, Germany</td>
			<td style="width:178px;"></td>
			<td>Face Camera (side view)</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:419px;">PIR uc 180 Thermal Camera</td>
			<td style="width:514px;">InfraTec GmbH, Dresden, Germany</td>
			<td style="width:178px;"></td>
			<td>Thermal Face Camera</td>
		</tr>
		<tr height="42">
			<td height="42" style="height:42px;width:419px;">Synchronization Hardware</td>
			<td style="width:514px;">Werkstatt, IIKT, University of Magdeburg, Magdeburg, Germany</td>
			<td style="width:178px;">custom built</td>
			<td style="width:422px;">Hardware triggering of cameras, trigger signal is recorded by BIOPAC and Audacity</td>
		</tr>
		<tr height="42">
			<td height="42" style="height:42px;width:419px;">Recording and Synchronization Software</td>
			<td style="width:514px;">Philipp Werner, Neuro-Information Technology, University of Magdeburg, Magdeburg, Germany</td>
			<td style="width:178px;">custom software</td>
			<td style="width:422px;">Real-time recording, offline video encoding, and offline synchronization</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:419px;">Examination Couch</td>
			<td style="width:514px;">ClinicalCare GmbH, Bremen, Germany</td>
			<td style="width:178px;"></td>
			<td style="width:422px;"></td>
		</tr>
		<tr height="42">
			<td height="42" style="height:42px;width:419px;">Ag-AgCl Electrodes EL254 / EL254S (Reusable, 4mm recording diameter)</td>
			<td style="width:514px;">BIOPAC Systems, Inc., Goleta, CA, USA</td>
			<td style="width:178px;"></td>
			<td>Used to record EMG M. corrugator and M. zygomaticus</td>
		</tr>
		<tr height="44">
			<td height="44" style="height:44px;width:419px;">Ag-AgCl Electrodes BlueSensor P (Disposable, skin contact size: 34 mm diameter, measuring area 154 mm2)</td>
			<td style="width:514px;">Ambu GmbH, Bad Nauheim, Germany</td>
			<td style="width:178px;"></td>
			<td>Used to record ECG and EMG M. trapezius. Also used for electrical stimulation</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:419px;">Audacity 2.1.2</td>
			<td style="width:514px;">Dominic Mazzoni (Audacity)</td>
			<td style="width:178px;"></td>
			<td>Audio Recording Software</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:419px;">Cold Gel Pack</td>
			<td style="width:514px;">C+V Pharma Depot GmbH, Versmold, Germany</td>
			<td style="width:178px;"></td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:419px;">Panthenol 50mg/g</td>
			<td style="width:514px;">ratiopharm GmbH, Ulm, Germany</td>
			<td style="width:178px;"></td>
			<td>Ointment</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:419px;">Alumnium Profiles</td>
			<td style="width:514px;">item Industrietechnik GmbH, Solingen, Germany</td>
			<td style="width:178px;"></td>
			<td>Used to install all cameras and microphone</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:419px;">Electrode Gel GEL1</td>
			<td style="width:514px;">BIOPAC Systems, Inc., Goleta, CA, USA</td>
			<td style="width:178px;"></td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:419px;">ELPREP Skin Preparation Gel</td>
			<td style="width:514px;">BIOPAC Systems, Inc., Goleta, CA, USA</td>
			<td style="width:178px;"></td>
			<td></td>
		</tr>
	</tbody>
</table>

multi-modal signals for analyzing pain responses to thermal and electrical stimuli

The assessment of pain relies mostly on methods that require a person to communicate. However, for people with cognitive and verbal impairments, existing methods are not sufficient as they lack reliability and validity. To approach this problem, recent research focuses on an objective pain assessment facilitated by parameters of responses derived from physiology, and video and audio signals. To develop reliable automated pain recognition systems, efforts have been made in creating multimodal databases in order to analyze pain and detect valid pain patterns. While the results are promising, they only focus on discriminating pain or pain intensities versus no pain. In order to advance this, research should also consider the quality and duration of pain as they provide additional valuable information for more advanced pain management. To complement existing databases and the analysis of pain regarding quality and length, this paper proposes a psychophysiological experiment to elicit, measure, and collect valid pain reactions. Participants are subjected to painful stimuli that differ in intensity (low, medium, and high), duration (5 s / 1 min), and modality (heat / electric pain) while audio, video (e.g., facial expressions, body gestures, facial skin temperature), and physiological signals (e.g., electrocardiogram [ECG], skin conductance level [SCL], facial electromyography [EMG], and EMG of M. trapezius) are being recorded. The study consists of a calibration phase to determine a subject&#8217;s individual pain range (from low to intolerable pain) and a stimulation phase in which pain stimuli, depending on the calibrated range, are applied. The obtained data may allow refining, improving, and evaluating automated recognition systems in terms of an objective pain assessment. For further development of such systems and to investigate pain reactions in more detail, additional pain modalities such as pressure, chemical, or cold pain should be included in future studies. Recorded data of this study will be released as the &#8220;X-ITE Pain Database&#8221;.

Pain is perceived differently by any person and may express itself diversely in facial expressions, paralinguistic and/or physiological signals. The design of this study is suitable to analyze pain responses in numerous ways with respect to the underlying aims. The obtained data may allow answering research questions, such as: Are there specific pain response patterns? Do they differ regarding pain model and duration?
A total of 134 subjects participated in our experiment. The sex ratio was 50/50. We divided them in the following age groups: 1) 18-29 years (N = 49, 23 men, 26 women), 2) 30-39 years (N = 45, 23 men, 22 women), 3) 40-50 years (N = 40, 21 men, 19 women). The average age of all subjects was 31.4 (SD = 9.7), of all men = 33.4 (SD = 9.3) and of all women = 32.9 (SD = 10.2) years. The study took place at the Department of Medical Psychology of the University of Ulm, Germany.
The main outcome of this protocol is a data set of audio, video and psychophysiological signals reflecting the subjects&#8217; responses to pain stimuli. Table 1 provides a general overview on the technical features of the recorded signals and on the numbers of induced pain stimuli in the study.&#160;
<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td colspan="5">Technical Features</td>
		</tr>
		<tr>
			<td>Signal:</td>
			<td colspan="2">Sampling Rate:</td>
			<td colspan="2">Attributes:</td>
		</tr>
		<tr>
			<td>Audio</td>
			<td colspan="2">44100 Hz</td>
			<td colspan="2">Mono, MP3 320 kbps</td>
		</tr>
		<tr>
			<td>Camera 1 (face, frontal view)</td>
			<td colspan="2">25 Hz</td>
			<td colspan="2">Color video: resolution 1384 x 1032, 
			HEVC encoded with libx265 (CRF 16, preset medium)</td>
		</tr>
		<tr>
			<td>Camera 2 (face, side view)</td>
			<td colspan="2">25 Hz</td>
			<td colspan="2">Color video: resolution 1620 x 840, 
			HEVC encoded with libx265 (CRF 16, preset medium)</td>
		</tr>
		<tr>
			<td>Body Camera</td>
			<td colspan="2">ca. 30 Hz</td>
			<td colspan="2">Color video: resolution 1500 x 600, 
			HEVC encoded with libx265 (CRF 16, preset medium); 
			Depth video: resolution 500 x 200, lossless encoding</td>
		</tr>
		<tr>
			<td>Thermal Camera</td>
			<td colspan="2">ca. 120.8 Hz</td>
			<td colspan="2">Surface temperature video: resolution 120 x 160, 
			grayscale MPEG-4-AVC encoded with&#160; libx264 
			(CRF 0,&#160; preset veryfast), 
			encoded temperature range 26.5-52.0 &#176;C (steps of 0.1)</td>
		</tr>
		<tr>
			<td>ECG</td>
			<td colspan="2">1000 Hz</td>
			<td colspan="2">Hardware filtered via BioPac: 35 Hz LP, 0.5 Hz HP, 
			50 Hz notch filter</td>
		</tr>
		<tr>
			<td>SCL</td>
			<td colspan="2">1000 Hz</td>
			<td colspan="2">Hardware filtered via BioPac: 10 Hz LP, no HP, 
			no notch filter</td>
		</tr>
		<tr>
			<td>EMG M. trapezius</td>
			<td colspan="2">1000 Hz</td>
			<td colspan="2">Hardware filtered via BioPac: 500 Hz LP, 10 Hz HP, 
			no notch filter</td>
		</tr>
		<tr>
			<td>EMG M. corrugator supercilii</td>
			<td colspan="2">1000 Hz</td>
			<td colspan="2">Hardware filtered via BioPac: 500 Hz LP, 10 Hz HP, 
			no notch filter</td>
		</tr>
		<tr>
			<td>EMG M. zygomaticus major</td>
			<td colspan="2">1000 Hz</td>
			<td colspan="2">Hardware filtered via BioPac: 500 Hz LP, 10 Hz HP, 
			no notch filter</td>
		</tr>
		<tr>
			<td>Stimuli</td>
			<td colspan="2">Thermal</td>
			<td colspan="2">Electrical</td>
		</tr>
		<tr>
			<td>Subjects:</td>
			<td>Phasic Stimuli (5 s):</td>
			<td>Tonic Stimuli (60 s):</td>
			<td>Phasic Stimuli (5 s):</td>
			<td>Tonic Stimuli (60 s):</td>
		</tr>
		<tr>
			<td>Per subject</td>
			<td>90 (30 per intensity)</td>
			<td>3 (1 per intensity)</td>
			<td>90 (30 per intensity)</td>
			<td>3 (1 per intensity)</td>
		</tr>
		<tr>
			<td>All (N = 134)</td>
			<td>12060 (4020 per intensity)</td>
			<td>402 (134 per intensity)</td>
			<td>12060 (4020 per intensity)</td>
			<td>402 (134 per intensity)</td>
		</tr>
		<tr>
			<td>Men (n = 67)</td>
			<td>6030 (2010 per intensity)</td>
			<td>201 (67 per intensity)</td>
			<td>6030 (2010 per intensity)</td>
			<td>201 (67 per intensity)</td>
		</tr>
		<tr>
			<td>Women (n = 67)</td>
			<td>6030 (2010 per intensity)</td>
			<td>201 (67 per intensity)</td>
			<td>6030 (2010 per intensity)</td>
			<td>201 (67 per intensity)</td>
		</tr>
	</tbody>
</table>
Table 1: Technical features and number of induced stimuli. The upper half (Technical Features) shows the sampling rates and attributes of the specific signals. The lower half (Stimuli) shows the numbers of the induced specific (thermal/electrical) pain stimuli for one subject, for all subjects and for each gender. (MP3 = Moving Picture Experts Group Layer-3 Audio, kbps = kilobits per second, HEVC = High Efficiency Video Coding, CRF = Constant Rate Factor, MPEG-4-AVC = Motion Picture Experts Group Layer-4 Video Advanced Video Coding, Hz = Hertz, &#176;C = degrees Celsius, s = seconds, ECG = Electrocardiogram, SCL = Skin Conductance Level, EMG = Electromyography, LP = low-pass filter, HP = high-pass filter, M. = Musculus).
A secondary outcome concerning the calibration phase of the study is presented in Table 2. It shows the mean stimulation temperatures and currents of pain intensities 1 and 3 (as calculated in step 5.11 of the protocol) for all subjects and additionally for the male and female subgroup.&#160;
<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td>Stimuli</td>
			<td colspan="4">Thermal [in &#176;C] mean (SD)</td>
			<td colspan="4">Electrical [in mA] mean (SD)</td>
		</tr>
		<tr>
			<td>Subjects</td>
			<td>pH1</td>
			<td>pH3</td>
			<td>tH1</td>
			<td>tH3</td>
			<td>pE1</td>
			<td>pE3</td>
			<td>tE1</td>
			<td>tE3</td>
		</tr>
		<tr>
			<td>All (N = 134)</td>
			<td>44.03 (2.25)</td>
			<td>49.17 (1.20)</td>
			<td>42.50 (2.14)</td>
			<td>47.76 (1.02)</td>
			<td>1.63 (0.94)</td>
			<td>5.64 (2.72)</td>
			<td>1.69 (1.12)</td>
			<td>5.70 (2.59)</td>
		</tr>
		<tr>
			<td>Men (n = 67)</td>
			<td>44.56 (2.18)</td>
			<td>49.48 (0.89)</td>
			<td>43.11 (1.98)</td>
			<td>47.93 (1.04)</td>
			<td>1.94 (1.01)</td>
			<td>6.83 (3.02)</td>
			<td>1.96 (1.16)</td>
			<td>6.90 (2.72)</td>
		</tr>
		<tr>
			<td>Women (n = 67)</td>
			<td>43.51 (2.74)</td>
			<td>48.87 (1.39)</td>
			<td>41.89 (2.14)</td>
			<td>47.59 (0.98)</td>
			<td>1.32 (0.75)</td>
			<td>4.45 (1.70)</td>
			<td>1.43 (1.01)</td>
			<td>4.51 (1.80)</td>
		</tr>
	</tbody>
</table>
Table 2: Mean stimulation temperatures and currents of pain intensities 1 and 3. (pH1, pH3 = phasic heat pain with intensity 1, 3; tH1, tH3 = tonic heat pain with intensity 1, 3; pE1, pE3 = phasic electrical pain with intensity 1, 3; tE1, tE3 = tonic electrical pain with intensity 1, 3; &#176;C = degrees Celsius; mA = milliampere, SD = standard deviation).
If all steps of the protocol are conducted carefully and no technical problems occur (in terms of computer or recording device crashes, etc.), a successful outcome may look similar as depicted in Figure 4. All signals are of high quality and not affected by external sources of interferences. The participant is clearly visible in every camera.&#160;
<img alt="Figure 4" class="xfigimg" src="/files/ftp_upload/59057/59057fig4.jpg" /> 
Figure 4: Example data from a successful experiment. The figure depicts recorded signals a few seconds before, during and after an intense pain stimulus. All signals are non-filtered and synchronized in time. For clarity, only representative screenshots of the video signals are shown here. (EMG = Electromyography, SCL = Skin Conductance Level, ECG = Electrocardiogram, M. = Musculus, s = seconds). <a href="https://www.jove.com/files/ftp_upload/59057/59057fig4large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
However, unexpected incidents may cause the data to become noisy or corrupted. Besides computer or recording device crashes, the coming-off of electrodes (especially reusable electrodes with small diameter which are attached by means of double-sided adhesive collars) mostly leads to unusable signals. As an example for a sub-optimal data set, Figure 5 shows the moment when an EMG electrode comes off and renders the corresponding signal useless.&#160;
<img alt="Figure 5" class="xfigimg" src="/files/ftp_upload/59057/59057fig5.jpg" /> 
Figure 5: Example data from a sub-optimal experiment. The red circle indicates the time one of the EMG electrodes (M. zygomaticus major) fell off the subject&#8217;s cheek. This might have been due to sweat or head movement. From this moment on, the signal was lost. (EMG = Electromyography, SCL = Skin Conductance Level, ECG = Electrocardiogram, M. = Musculus, s = seconds). <a href="https://www.jove.com/files/ftp_upload/59057/59057fig5large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
Due to ethical guidelines, the maximum intensities of thermal and electrical stimuli had to be restricted. Regarding thermal calibration control part (see supplementary file), 37 subjects (31 men, 6 women) reached the given cutoff of 50.5 &#176;C (ratio = 37/134 = 27.61 %). As for thermal calibration part 1, 60 participants (39 men, 21 women) reached the cutoff of 50.0 &#176;C (ratio = 60/134 = 44.78 %) and concerning part 2, 57 persons (37 men, 20 women) reached the cutoff of 49.5 &#176;C (ratio = 57/134 = 42.54 %). The cutoff for both electrical calibration parts was 25 mA. None of the 134 subjects reached it.
As we plan to publish the data (see next paragraph), the data sets of participants who have reached the cutoffs will additionally be marked and their subjective pain ratings for the corresponding cutoffs will be included.
We would like to point out that the main focus of the protocol is obtaining multi-modal signals for analyzing thermal and electrical pain. Therefore, no other results are discussed here. After checking and excluding data sets due to missing data or rejected written consent for data sharing, the data sets of this study will be made available under the name &#8220;X-ITE Pain Database&#8221;. For further information on when and how to obtain the X-ITE Pain DB please visit https://github.com/philippwerner/pain-database-list.
Supplementary File 1.&#160;<a href="https://www.jove.com/files/ftp_upload/59057/Supplementary_File_1.docx" target="_blank">Please click here to download this file.</a>

Watch this Scientific Journal Video about Multi-Modal Signals for Analyzing Pain Responses to Thermal and Electrical Stimuli at JoVE.com

Multi-Modal Signals for Analyzing Pain Responses to Thermal and Electrical Stimuli

This article focuses on the experimental elicitation of pain through heat (thermal) and electrical stimulation while recording physiological, visual, and paralinguistic responses. It aims at collecting valid multimodal data for analyzing pain based on its intensity, quality, and duration.&#160;

The assessment of pain relies mostly on methods that require a person to communicate. However, for people with cognitive and verbal impairments, existing ...

multi-modal-signals-for-analyzing-pain-responses-to-thermal

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10.4K Views.  Ulm University. This article focuses on the experimental elicitation of pain through heat (thermal) and electrical stimulation while recording physiological, visual, and paralinguistic responses. It aims at collecting valid multimodal data for analyzing pain based on its intensity, quality, and duration. 

Video: Multi-Modal Signals for Analyzing Pain Responses to Thermal and Electrical Stimuli

Simultaneous Scalp Electroencephalography (EEG), Electromyography (EMG), and Whole-body Segmental Inertial Recording for Multi-modal Neural Decoding

Recent studies support the involvement of supraspinal networks in control of bipedal human walking. Part of this evidence encompasses studies, including our previous work, demonstrating that gait kinematics and limb coordination during treadmill walking can be inferred from the scalp electroencephalogram (EEG) with reasonably high decoding accuracies. These results provide impetus for development of non-invasive brain-machine-interface (BMI) systems for use in restoration and/or augmentation of gait- a primary goal of rehabilitation research. To date, studies examining EEG decoding of activity during gait have been limited to treadmill walking in a controlled environment. However, to be practically viable a BMI system must be applicable for use in everyday locomotor tasks such as over ground walking and turning. Here, we present a novel protocol for non-invasive collection of brain activity (EEG), muscle activity (electromyography (EMG)), and whole-body kinematic data (head, torso, and limb trajectories) during both treadmill and over ground walking tasks. By collecting these data in the uncontrolled environment insight can be gained regarding the feasibility of decoding unconstrained gait and surface EMG from scalp EEG.

Simultaneous Scalp Electroencephalography EEG, Electromyography EMG, and Whole-body Segmental Inertial Recording for Multi-modal Neural Decoding

Development of an effective brain-machine-interface (BMI) system for restoration and rehabilitation of bipedal locomotion requires accurate decoding of user's intent. Here we present a novel experimental protocol and data collection technique for simultaneous non-invasive acquisition of neural activity, muscle activity, and whole-body kinematics during various locomotion tasks and conditions.

Recent  studies support the involvement of supraspinal networks in control of bipedal  human walking. Part of this evidence  encompasses studies, ...

Irrelevant Stimuli and Action Control: Analyzing the Influence of Ignored Stimuli via the Distractor-Response Binding Paradigm

Selection tasks in which simple stimuli (e.g. letters) are presented and a target stimulus has to be selected against one or more distractor stimuli are frequently used in the research on human action control. One important question in these settings is how distractor stimuli, competing with the target stimulus for a response, influence actions. The distractor-response binding paradigm can be used to investigate this influence. It is particular useful to separately analyze response retrieval and distractor inhibition effects. Computer-based experiments are used to collect the data (reaction times and error rates). In a number of sequentially presented pairs of stimulus arrays (prime-probe design), participants respond to targets while ignoring distractor stimuli. Importantly, the factors response relation in the arrays of each pair (repetition vs. change) and distractor relation (repetition vs. change) are varied orthogonally. The repetition of the same distractor then has a different effect depending on response relation (repetition vs. change) between arrays. This result pattern can be explained by response retrieval due to distractor repetition. In addition, distractor inhibition effects are indicated by a general advantage due to distractor repetition. The described paradigm has proven useful to determine relevant parameters for response retrieval effects on human action.

The distractor-response binding paradigm is described. It can be used to shed light on the influence irrelevant stimuli, competing with targets for a response, can have on human action. Both response retrieval effects and distractor inhibition effects can be analyzed within the paradigm.

Selection tasks in which simple stimuli (e.g. letters) are presented and a target stimulus has to be selected against one or more distractor stimuli are ...

Determining Pain Detection and Tolerance Thresholds Using an Integrated, Multi-Modal Pain Task Battery

Human pain models are useful in the assessing the analgesic effect of drugs, providing information about a drug&#39;s pharmacology and identify potentially suitable therapeutic populations. The need to use a comprehensive battery of pain models is highlighted by studies whereby only a single pain model, thought to relate to the clinical situation, demonstrates lack of efficacy. No single experimental model can mimic the complex nature of clinical pain. The integrated, multi-modal pain task&#160;battery presented here encompasses the electrical stimulation task, pressure stimulation task, cold pressor task, the UVB inflammatory model which includes a thermal task and a paradigm for inhibitory conditioned pain modulation. These human pain models have been tested for predicative validity and reliability both in their own right and in combination, and can be used repeatedly, quickly, in short succession, with minimum burden for the subject and with a modest quantity of equipment. This allows a drug to be fully characterized and profiled for analgesic effect which is especially useful for drugs with a novel or untested mechanism of action.

Human pain models are valuable tools used to assess the analgesic potential of novel compounds and predict their clinical efficacy, especially when used in an integrated manner. Although implementation of these models is complex, with proper execution, the pain models described in this protocol can provide predictive and reliable results.

Human pain models are useful in the assessing the analgesic effect of drugs, providing information about a drug&#39;s pharmacology and identify ...

A Protocol of Manual Tests to Measure Sensation and Pain in Humans

Numerous qualitative and quantitative techniques can be used to test sensory nerves and pain in both research and clinical settings. The current study demonstrates a quantitative sensory testing protocol using techniques to measure tactile sensation and pain threshold for pressure and heat using portable and easily accessed equipment. These techniques and equipment are ideal for new laboratories and clinics where cost is a concern or a limiting factor. We demonstrate measurement techniques for the following: cutaneous mechanical sensitivity on the arms and legs (von-Frey filaments), radiant and contact heat sensitivity (with both threshold and qualitative assessments using the Visual Analog Scale (VAS)), and mechanical pressure sensitivity (algometer, with both threshold and the VAS). The techniques and equipment described and demonstrated here can be easily purchased, stored, and transported by most clinics and research laboratories around the world. A limitation of this approach is a lack of automation or computer control. Thus, these processes can be more labor intensive in terms of personnel training and data recording than the more sophisticated equipment. We provide a set of reliability data for the demonstrated techniques. From our description, a new laboratory should be able to set up and run these tests and to develop their own internal reliability data.

The goal of this procedure is to demonstrate a battery of quantitative techniques for sensory and pain measurement in humans. The equipment and techniques described are commonly found in pain clinics or are easy to obtain.

Numerous qualitative and quantitative techniques can be used to test sensory nerves and pain in both research and clinical settings. The current study ...

A Method for Manipulating Blood Glucose and Measuring Resulting Changes in Cognitive Accessibility of Target Stimuli

Much research in social psychology has investigated the impact of bodily energy need on cognition and decision-making. As such, blood glucose, the body's primary energy source, has been of special interest to researchers for years. Fluctuations in blood glucose have been linked to a variety of changes in cognitive and behavioral processes, such as self-control, political attitudes, and eating behavior. To help meet growing interest in the links between bodily energy need and these processes, this manuscript offers a simple methodology to experimentally manipulate blood glucose using a fasting procedure followed by administration of a sugar-sweetened, unsweetened, or artificially-sweetened beverage. This is followed by presentation of a method for measuring resulting changes in implicit cognition using a lexical decision-task. In this task, participants are asked to identify whether strings of letters are words or non-words and response latencies are recorded. Sample results from a recent publication are presented as an example of the applications for the experimental manipulation of blood glucose and the lexical decision task measures.

The authors introduce a method for manipulating blood glucose and measuring resulting changes in cognitive accessibility of target words using a lexical decision task.

Much research in social psychology has investigated the impact of bodily energy need on cognition and decision-making.  As such, blood glucose, the body's ...

Simultaneous Recordings of Cortical Local Field Potentials and Electrocorticograms in Response to Nociceptive Laser Stimuli from Freely Moving Rats

Electrocortical responses, elicited by laser heat pulses that selectively activate nociceptive free nerve endings, are widely used in many animal and human studies to investigate the cortical processing of nociceptive information. These laser-evoked brain potentials (LEPs) consist of several transient responses that are time-locked to the onset of laser stimuli. However, the functional properties of the LEP responses are still largely unknown, due to the lack of a sampling technique that can simultaneously record neural activities at the surface of the cortex (i.e., electrocorticogram [ECoG] and scalp electroencephalogram [scalp EEG]) and inside the brain (i.e., local field potential [LFP]). To address this issue, we present here an animal protocol using freely moving rats. This protocol is composed of three main procedures: (1) animal preparation and surgical procedures, (2) a simultaneous recording of ECoG and LFP in response to nociceptive laser stimuli, and (3) data analysis and feature extraction. Specifically, with the help of a 3D-printed protective shell, both ECoG and LFP electrodes implanted on the rat&#39;s skull were securely held together. During data collection, laser pulses were delivered on the rat&#39;s forepaws through gaps in the bottom of the chamber when the animal was in spontaneous stillness. Ongoing white noise was played to avoid the activation of the auditory system by the laser-generated ultrasounds. As a consequence, only nociceptive responses were selectively recorded. Using the standard analytical procedures (e.g., band-pass filtering, epoch extraction, and baseline correction) to extract stimulus-related brain responses, we obtained results showing that LEPs with a high signal-to-noise ratio were simultaneously recorded from ECoG and LFP electrodes. This methodology makes the simultaneous recording of ECoG and LFP activities possible, which provides a bridge of electrocortical signals at the mesoscopic and macroscopic levels, thereby facilitating the investigation of nociceptive information processing in the brain.

We developed a technique that simultaneously records both electrocorticography and local field potentials in response to nociceptive laser stimuli from freely moving rats. This technique helps establish a direct relationship of electrocortical signals at the mesoscopic and macroscopic levels, which facilitates the investigation of nociceptive information processing in the brain.

Electrocortical responses, elicited by laser heat pulses that selectively activate nociceptive free nerve endings, are widely used in many animal and ...

Generating Strictly Controlled Stimuli for Figure Recognition Experiments

This protocol introduces a method for generating strictly controlled and objectively defined stimuli for figure recognition experiments. A (6, n) figure consists of n line segments that are spanned between n pairs of points located at the vertices of an invisible regular hexagon. The structural properties (graph invariants) and superficial features (non-graph invariants) of each (6, n) figure with n values ranging from 1 to 6 are calculated and stored in a database. Using this database, experimenters can systematically extract appropriate figures depending on the purpose of the experiment. Furthermore, if the database does not contain necessary information, new feature values can sometimes be calculated ad hoc from the formation of a specific (6, n) figure. Let us call a mirror-reflected pair of figures an axisymmetric (Ax) pair. An Ax pair of figures is known to be more difficult to discriminate than a non-identical pair in the decision of whether the shapes of a given pair are rotated-to-be-identical (Idr). The purpose of the present experiment is to examine whether the sameness of line lengths between two figures in a pair causes the discrimination of the pair to be as difficult as that of an Ax pair. Mutually isomorphic figures share common structural properties despite differences in shape. Ax pairs and Idr pairs are special cases of isomorphic pairs. Furthermore, an Ax pair and Idr pair share most of the superficial feature values, except the relative direction from one location to another location across an axis of symmetry is opposite for an Ax pair. Three types of mutually isomorphic (6, 4) figure pairs were generated: Idr; Ax; and non-identical, non-axisymmetric, isomorphic (Nd) pairs. Nd pairs were further classified into three subcategories according to the superficial feature values of the degree of line length differences.

This protocol describes a method for an experiment that examines whether specific graph and non-graph properties (features) are relevant to the recognition of figures. The method uses a database that stores various feature values of respective figures called (6 point, n line) figures.

This protocol introduces a method for generating strictly controlled and objectively defined stimuli for figure recognition experiments. A (6, n) figure ...

An Instrumented Pull Test to Characterize Postural Responses

Impairment of postural reflexes, termed postural instability, is a common and disabling deficit in Parkinson's disease. To assess postural reflexes, clinicians typically employ the pull test to grade corrective responses to a backward perturbation at the shoulders. However, the pull test is prone to issues with reliability and scaling (score/4). Here, we present an instrumented version of the pull test to more precisely quantify postural responses. Akin to the clinical test, pulls are manually administered except pull force is also recorded. Displacements of the trunk and feet are captured by a semi-portable motion tracking system. Raw data represent distance traveled (in millimeter units), making subsequent interpretation and analysis intuitive. The instrumented pull test also detects variabilities influencing pull test administration, such as pull force, thereby identifying and quantifying potential confounds that can be accounted for by statistical techniques. The instrumented pull test could have application in studies seeking to capture early abnormalities in postural responses, track postural instability over time, and detect responses to therapy.

Impairment of postural reflexes, termed postural instability, is difficult to quantify. Clinical assessments such as the pull test suffer issues with reliability and scaling. Here, we present an instrumented version of the pull test to objectively characterize postural responses.

Impairment of postural reflexes, termed postural instability, is a common and disabling deficit in Parkinson's disease. To assess postural reflexes, ...

Using Looming Visual Stimuli to Evaluate Mouse Vision

The visual system in the central nervous system processes diverse visual signals. Although the overall structure has been characterized from the retina through the lateral geniculate nucleus to the visual cortex, the system is complex. Cellular and molecular studies have been conducted to elucidate the mechanisms underpinning visual processing and, by extension, disease mechanisms. These studies may contribute to the development of artificial visual systems. To validate the results of these studies, behavioral vision testing is necessary. Here, we show that the looming stimulation experiment is a reliable mouse vision test that requires a relatively simple setup. The looming experiment was conducted in a large enclosure with a shelter in one corner and a computer monitor located on the ceiling. A CCD camera positioned next to the computer monitor served to observe mouse behavior. A mouse was placed in the enclosure for 10 minutes and allowed to acclimate to and explore the surroundings. Then, the monitor projected a program-derived looming stimulus 10 times. The mouse responded to the stimuli either by freezing or by fleeing to the hiding place. The mouse&#39;s behavior before and after the looming stimuli was recorded, and the video was analyzed using motion tracking software. The velocity of the mouse movement significantly changed after the looming stimuli. In contrast, no reaction was observed in blind mice. Our results demonstrate that the simple looming experiment is a reliable test of mouse vision.

To examine mouse vision, we conducted a looming test. Mice were placed in a large square arena with a monitor on its ceiling. The looming visual stimulus consistently evoked freezing or flight reactions in the mice.

The visual system in the central nervous system processes diverse visual signals. Although the overall structure has been characterized from the retina ...

A Cross-Disciplinary and Multi-Modal Experimental Design for Studying Near-Real-Time Authentic Examination Experiences

Over the past ten years, research into students&#39; emotions in educational environments has increased. Although researchers have called for more studies that rely on objective measures of emotional experience, limitations on utilizing multi-modal data sources exist. Studies of emotion and emotional regulation in classrooms traditionally rely on survey instruments, experience-sampling, artifacts, interviews, or observational procedures. These methods, while valuable, are mainly&#160;dependent on participant or observer subjectivity and is limited in its authentic measurement of students&#39; real-time performance to a classroom activity or task. The latter, in particular, poses a stumbling block to many scholars seeking to objectively measure emotions and other related measures in the classroom, in real-time.
The purpose of this work is to present a protocol to experimentally study students&#39; real-time responses to exam experiences during an authentic assessment situation. For this, a team of educational psychologists, engineers, and engineering education researchers designed an experimental protocol that retained the limits required for accurate physiological sensor measurement, best-practices of salivary collection, and an authentic testing environment. In particular, existing studies that rely on physiological sensors are conducted in experimental environments that are disconnected from educational settings (e.g., Trier Stress Test), detached in time (e.g., before or after a task), or introduce analysis error (e.g., use of sensors in environments where students are likely to move). This limits our understanding of students&#39; real-time responses to classroom activities and tasks. Furthermore, recent research has called for more considerations to be covered around issues of recruitment, replicability, validity, setups, data cleaning, preliminary analysis, and particular&#160;circumstances (e.g., adding a variable in the experimental design) in academic emotions research that relies on multi-modal approaches.

An experimental design was developed to investigate the real-time influences of an examination experience to assess the emotional realities students experience in higher education settings and tasks. This design is the result of a cross-disciplinary (e.g., educational psychology, biology, physiology, engineering) and multi-modal (e.g., salivary markers, surveys, electrodermal sensor) approach.

Over the past ten years, research into students&#39; emotions in educational environments has increased. Although researchers have called for more studies ...

Methods Article

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