Name: a novel experimental and analytical approach to the multimodal neural decoding of intent during social interaction in freely-behaving human infants
Uploaded: 2015-10-04T14:00:00
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Description: Watch this Scientific Journal Video about A Novel Experimental and Analytical Approach to the Multimodal Neural Decoding of Intent During Social Interaction in Freely-behaving Human Infants at JoVE.co

This work was supported by Eunice Kennedy Shriver National Institutes of Child Health &#38; Human Development (NICHD) Award # P01 HD064653-01. The content is solely the responsibility of the authors and does not necessarily represent the official views of the NICHD or the National Institutes of Health.

The following protocol was examined and approved by the Institutional Review Board at the University of Houston. All infant subjects&#8217; parents or guardians received, read, and signed a consent form prior to participation. Parents received free parking and a $20 gift card as compensation for their participation in the study whereas the infants chose an age-appropriate toy.
The recruited infant subjects met the following criteria: 1) Age between 6 and 24 months. 2) Infant was healthy, had normal growth and development, and had no history of natal issues, concussions, seizures, strokes, or learning disabilities. Examples of some common developmental disabilities that would exclude an infant from testing are failure to thrive, undernourishment, and use of alcohol or drugs by the mother during pregnancy.
A simplified flow diagram for the simultaneous recording of multimodal neural activity via scalp EEG and IMUs in freely behaving infants is&#160;presented in Figure 1.
1. Informed Consent
<ol>
	<li>Show parents the experimental room and briefly explain the purpose of the experiment to them.</li>
</ol>
2. Scalp EEG Preparation
<ol>
	<li>Note: The EEG system used (Table 1) consists of an active electrode system with removable electrodes. Impedance levels are indicated on the electrodes using LEDs.</li>
	<li>Measure the infant&#8217;s head circumference in cm. Place a measuring tape around the widest part of the head, passing it over the eyebrows and around the occipital prominence in the back of the head. 
	Note: Measuring the head circumference is necessary for selecting an appropriately sized EEG cap. There are special sizes for the infant population described in Table 1.</li>
	<li>Place electrodes on an appropriately sized EEG cap (Table 1) as specified by the 10-20 international system. The cap ensures proper placement of the electrodes on the scalp. 
	Note: It is preferable to have the cap ready by the time the infant arrives to the study location. Information about the head size can be obtained from the parents before time, or approximated using tables available online [http://www.cdc.gov/growthcharts/html_charts/hcageinf.htm]. If the head measurement does not correspond to the researcher&#8217;s approximation, a new cap must be prepared. If multiple caps and electrodes are available, multiple caps can be set up in advance to minimize preparation time.</li>
	<li>Explain the gelling procedure to the parent. Show them the blunt needle and syringe used to apply gel to each electrode and let them feel the needle on their skin. Use a demo EEG electrode and scalp cap to go over the gelling procedure.</li>
	<li>Measure the distance from the nasion to the inion along the midsagittal plane of the surface of the scalp. Proceed to fit the scalp cap from behind the infant.</li>
	<li>Align the Cz electrode with the vertex of the head. Proceed to align and center the Fp1 and Fp2 electrodes on the forehead at approximately 10% of the distance between the nasion and the inion. Align the scalp cap symmetrically along the midsagittal plane of the head. Make sure mid-electrodes [AFz, Fz, FCz, Cz, CPz, Pz, POz, Oz] are aligned with the nasion and inion. When done, secure the cap by strapping below the chin. 
	Note: Distract the infant while he/she is being fitted with the EEG cap. An age appropriate video is commonly used to distract infants during setup.</li>
	<li>&#160;Connect the reference, ground, and recording electrodes to the control box. Turn on the impedance indicators from the control box.&#160;</li>
	<li>Starting with ground and reference electrodes, use a small syringe to inject electrolyte gel into the space between the scalp and electrode until the impedance of each electrode measures below 60 k&#937;. This is indicated by a yellow or green light on the electrodes. Further details on EEG electrode preparation are available in 4. 
	Note: During the gelling procedure, the infant might move his/her head for various reasons (curiosity, fear, diverted attention). It is therefore recommended that the second experimenter or parent continues to distract the infant. Use the syringes from behind the infant&#8217;s head to avoid the risk of hitting the infant&#8217;s face with the needle due to unexpected movement of the subject.</li>
	<li>Connect the amplifiers to the host PC via a USB port using a fiber-optic to USB converter. 
	Note: Figure 2A shows the infant subject setup with the EEG cap and the IMU placement. Notice that the electrode cables and the control box are held up, providing load-free mobility to the subject. Figure 2B displays a topographic view of the EEG cap&#8217;s electrode locations following the 10-20 labeling system.</li>
</ol>
3. IMUs Preparation
<ol>
	<li>Open the IMU software. Click on &#8220;New&#8221; on the graphical interface.&#160;</li>
	<li>Then click &#8220;Configure&#8221; in the dialog box to configure IMUs. Set the sampling frequency to 128 Hz.</li>
	<li>Secure the IMUs on the head, chest, and left and right wrists of the infant. 
	Note: See Figure 2A. In parallel, fit the experiment&#8217;s wrists with IMUs. Make sure IMUs are secured well to the body to minimize inertial measurement errors.</li>
	<li>Using straps, place the wrist IMUs on the dorsal side of the wrists. Place the chest IMU on the ventral surface near the center of the chest using a harness.</li>
	<li>Attach the head IMU to the chin straps of the scalp cap so that it is near the left side of the chin. Position the IMUs so that they face outward, with the LED light oriented up. 
	Note: The IMUs each weigh about 22 g, and their small size and light weight are unlikely to impede movement. An additional experimenter may be needed to distract the infant for reasons described in step 2.8.</li>
</ol>
4. Video Capture and Synchronization of Data Streams
<ol>
	<li>Place video camera (18) so that the infant (12), the actor (14), and the LED trigger (13) are all clearly visible. See Figure 4. 
	Note: Video recordings are used to allow visual annotation and segmentation of behaviors triggered by the experimenter, as well as confirmation of the beginning and end of the recording session.</li>
	<li>Connect the trigger input/output enclosure (7) to the EEG (5) and IMUs (4). See Figure 3. 
	Note: A custom input/output enclosure (7), shown in Figure 3, was designed for aligning all recorded data streams (EEG, IMUs, and video) and thus use event markers from one recording (such as video) or aid the analysis of another recording (such as EEG or acceleration). This is accomplished by using a single momentary pushbutton that, when depressed, will send an active-low (+V to Ground) trigger pulse to EEG (via a DB-25 parallel port), IMU (via a mini-DIN-6 connector), and video (by illuminating a light-emitting diode (LED) positioned in view of the video camera) recordings simultaneously. A 7404 IC logic invertor chip was used to transform the low-state (Ground) to a high-state (+V) pulse, providing the voltage potential needed to power the LED.</li>
	<li>Have an experimenter in close proximity to the trigger pushbutton and the data collection computer. The experimenter is in charge of operating the trigger, monitoring the data quality throughout the experiment, and saving the data.</li>
</ol>
5. Preparation of Testing Environment
<ol>
	<li>See Figure 4. </li>
</ol>
6. Data Collection
<ol>
	<li>Record the initial impedance values of the EEG electrodes using the control software by selecting the &#8220;Impedance Check&#8221; tab, clicking the radio button &#8220;Impedance On&#8221; (wait about 5 sec&#160;for electrode impedance values to stabilize), and clicking the &#8220;Save impedance&#8221; radio button to save impedances. See Figure 5.</li>
	<li>No filters are applied during EEG data collection.</li>
	<li>In the EEG recorder program, click on &#8220;Monitor&#8221; then &#8220;Play&#8221; to start recoding EEG data. See Figure 6.</li>
	<li>In the IMU software, click &#8220;Stream&#8221; and then &#8220;Record&#8221; in the dialog box to start recording IMU data. See Figure 7.</li>
	<li>Apply three triggers (III) to signal the start of the experiment using the pushbutton on the input/output enclosure (7). Let the infant rest for 1 min while recording. This provides initial baseline data.</li>
	<li>Perform experiment and allow the infant subject take breaks as needed. Each experiment trial consists of a turn-taking task where the actor shows an action to the infant using an object (usually, but not always, a toy), passes it to the infant in an effort to initiate an imitation response, and finally retrieves the object back from the infant. 
	Note: This exchange is generally repeated for 4-5 trials or until the infant shows no further interest in the object.</li>
	<li>Given the environment of the session, annotate&#160;all timings of each behavior after data collection using the video recording.</li>
	<li>Record the final impedance values of the EEG electrodes. See step 6.1.</li>
	<li>Digitize the 3D spatial coordinates of the EEG electrodes using the EEG electrode 3D scanner and software and its corresponding software.</li>
	<li>Start the 3D scanning software. Go to &#8220;File&#8221; and select &#8220;new workspace&#8221;. Load the electrode position file in the first tab of the workspace and provide the necessary information in the storage tab.</li>
	<li>In the scanning toolbar, click on &#8220;start scanning&#8221;. Use the 3D scanner, at approximately 30 cm&#160;from the infant&#8217;s head, to scan the electrode locations by following the changing light patterns on the cap. Once the scanning is complete, the software will automatically save the results. 
	Note: At the back of the head, the cables may block the LEDs on the electrodes due to the small size of the infant&#8217;s head. The software allows the user to remove some electrodes from the scan without compromising the results. Remove the occipital electrodes from the scan if necessary.</li>
</ol>
7. Behavioral Segmentation
<ol>
	<li>Inspect the video recording of the experiment session and mark the time when the trigger LED blinks (i.e., when the experiment begins and ends), and when any of the previously mentioned behaviors in step 6.6&#160;start and end.</li>
</ol>
8. Source Imaging
<ol>
	<li>Note: Source imaging can accurately identify the generators of EEG potentials within the brain and it usually consists of solving the forward and the inverse problem 5.</li>
	<li>Acquire High resolution T1 weighted Magnetic Resonance Image (MRI) data from public domain databases such as the Neurodevelopmental MRI database which contains average MRI templates as a function of age (in months) for the first two years of life 6,7. 
	Note: This study uses only age-appropriate forward head models of infants and therefore sex information is not taken into account when selecting the MRI template.)</li>
	<li>&#160;Acquire source volumes for boundary element models (BEM) that include head compartments (i.e., gray matter, white matter, scalp, inner or outer skull) as MRI volumes from the Neurodevelopmental MRI database 8.</li>
	<li>Pre-process BEM MRI volume to obtain a realistic head model by extracting the surface of the brain and head compartments in Curry 7, or similar brain imaging software package.</li>
	<li>&#160;Import the T1 weighted MRI. Manually identify the three fiducial points such as right/left pre-auricular points and nasion within the brain imaging software, or similar package.</li>
	<li>&#160;Co-register EEG and MRI space to obtain the rigid geometrical transformation between the MRI&#8216;s space and the subject&#8216;s space by fitting the fiducial points indicated on MRI and the fiducials obtained with 3D scanner in steps&#160;6.9-6.11&#160;using the brain imaging software or similar package.</li>
	<li>Solve the forward model. 
	Note: The forward problem involves the definition of a head model that represents the geometry of the head and of the electrical conductivity properties of the head volume 9,10.</li>
	<li>Solve the inverse problem 
	Note: The inverse problem tries to infer a location, strength and a time course of a source in the brain from the scalp EEG signals by using signal processing techniques 11.</li>
	<li>Segment continuous EEG data into trials around the task onset (e.g., imitation, observation segments) with pre- and post-task onset segments of 2 sec&#160;length. Identify channels with high impedance as &#8220;bad channels&#8221; and other artifactual trials in each trial. Pre-process the EEG data for artifact rejection using ICA and reconstruct the bad channels with the average of neighboring artifact-free channels. 12,13,14.</li>
	<li>Use the forward model as an input to an automated optimization algorithm that searches over the entire brain to identify the best possible source locations. &#160;</li>
	<li>Visually analyze the estimated sources and pick up the solutions that most match your expectation based on prior knowledge from the nature of experiment and interpret them carefully. 
	Note: This step is the most biased one due to the ill-posed nature of the inverse problem, that is, different configurations of the sources may result in the same surface potentials. Therefore, it may be helpful to perform a sanity check to verify that a given head model and inverse method works well.</li>
	<li>Perform a sanity check, localizing a known simulated dipole. Namely, for the given head model place a dipole with known configuration, and solve the forward problem to obtain simulated voltages for this dipole.</li>
	<li>Solve the inverse problem with these voltages on the same head model, to make sure it returns the simulated dipoles. 
	Note: These sanity checks should return the dipoles initially placed to a high degree of accuracy.</li>
</ol>

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Brain Products and Brain Vision kindly financed the open access fees charged by the journal.

The protocol described presents a methodology to collect data from freely-behaving infants while they are interacting with an experimenter in real time. It employs mobile brain imaging technology (scalp EEG) to capture neural activity while simultaneously recording kinematic data with IMUs in strategic body locations. The experiment session is also recorded by a video camera. The three data recording systems are synchronized using a custom trigger system.
The EEG and IMU system are strapped to the subject as he/she is freely moving during the experiment session. The IMUs need to be strapped securely to be able to capture kinematics accurately. To ensure full unconstrained mobility of the subject, the equipment has to be as minimally intrusive as possible; thus the use of the holder to support the EEG&#8217;s electrode cables and the EEG control box. The experimenter then interacts with the infant for approximately 15 minutes. The infant will elicit an age-dependent repertoire of behaviors during the interaction. These include rest, reach-grasp, reach-offer, explore, observe, and imitate. However, some infants will be unwilling to cooperate in the session due to fatigue, lack of comfort, or stress. Make sure to schedule the experiment when the child is most dynamic and active to prevent the occurence of negative responses from him/her.
The nature of the experiment presents risks to the quality of the data recorded throughout the session. Therefore, it is crucial to test all the connections and data quality before starting the recording session, and to monitor them continuously during the session. If the data EEG system is not recording quality data, stop the software and unplug all connections. Before restarting the software or connecting the equipment back to the laptop, remove all possible noise sources (i.e., power supplies) from the proximity of the recording hardware. The EEG hardware includes signal amplifiers that can pick up environmental noise if placed close to electrical noise sources. For the IMU receiver, make sure there is no interference in the line of sight between the receiver and the experiment and infant.
This experimental setup provides high temporal resolution neural data by measuring electrical activity at the surface of the scalp. Recent studies have demonstrated the feasibility of utilizing these signals, together with whole-body kinematics, to identify classifiable information for expressive movements 20, and functional movements 21,22, suggesting that this proposed data collection approach could lead to a better understanding of the neural basis of imitation in infants.
Recent contributions featuring powerful machine-learning algorithms applied to brain dynamics 13,20,21 are building a growing toolkit to study surface potentials in more natural settings. This proposed setup provides a spectrum of possibilities for research questions to be addressed 2,22. Particularly, it can be applied to research focused on a) understanding the neural basis of cognitive-motor development of infants based on a large population of subjects; b) understanding the neural basis of the baby&#8217;s intent in &#8216;action and context&#8217;, which should be predictive of the incoming behavioral action; c) quantifying common and unique neural patterns to characterize individuality and variability in the developing brain; and d) studying the emergence of imitation and learning processes. These goals entail the deployment of machine learning algorithms that can deal with statistically rich data both in informative brain-originated potentials and movement or muscle artifacts 12,20,23 .
This study attempts to estimate the cortical sources and electric&#160;field potentials using the infant EEG data. Due to the technical difficulties such as the lack of knowledge in infant head conductivity values and thickness of cortical matter, the accurate modeling of the head model is a difficult task. Further studies are needed for noninvasive regional tissue conductivity estimates in infants 24. Cortical surface segmentation of infant MRI data presents an additional challenge due to the poor contrast found in images of the developing human brain&#160;25. Future research is needed to address these difficulties and estimate various neurophysiological correlates of infant development and behavior.
Finally, the proposed experimental protocol and methods could be deployed in the study of those with developmental disabilities such as infants with probable autism spectrum disorder (ASD). In such an application, it would be desirable to include a control group and suitable developmental assessments to characterize the two groups (control and ASD). For example, a study group could consist of all high-risk (for ASD) infant siblings assessed with the Autism Diagnostic Observation Schedule 26, symptom severity 27 and the Mullen Scales of Early Learning 28 to characterize general cognitive ability. If available, diffusion-weighted MRI scans would also be highly desirable 29.

One of the fundamental human capabilities is the ability to learn and deploy actions (action production) strategically in service of goals and rewards, the ability to apprehend the goals of social partners (action understanding) in order to produce appropriate social responses, and the ability to learn from others through observation and imitation 1. The neural basis of these cognitive-motor capabilities have been attributed, at least in part, to the so-called mirror neuron system; a system that is thought to be engaged when one views someone performing an action and when one performs the action. However, the potential link between the mirror neuron system and action understanding is not yet well understood 1. Studying the emergence and development of this mirror neuron system in human infants has been hampered by a) the technical limitations of multi-modal data acquisition of brain activity correlated to intent and fine-grained motion data, b) the constraints imposed by experimental protocols that are unnatural (e.g., social interaction with an agent depicted in a videotape, the need for maintaining a still posture to minimize artifacts during Electroencephalographic (EEG) recordings, etc.), and c) the communication/language barriers when testing young infants/toddlers that greatly limit the researcher&#8217;s capabilities to give instructions and validate behaviors.
For a better understanding of the varying neural and behavioral dynamics in natural behavior, we developed a novel experimental and analytical approach that allows the time-resolved study of the neural substrates of emerging goal-oriented and social behaviors in young children. Specifically, we deployed an EEG based mobile brain imaging (MoBI) approach 2 to record brain activity and movement from freely-behaving infants during interaction with an experimenter. Inertial measurement units (IMUs) were used to monitor subject and experimenter&#8217;s kinematics.
EEG technology and inertial sensors were used to study neural patterns and activations associated to the infants&#8217; action imitation and goal-oriented behaviors in an unscripted interaction with an experimenter/actor. Actions such as reach-grasp, reach-offer, observe, rest, and explore are all part of the cognitive-motor processes involved in imitation. Furthermore, we use source estimation to localize the generators of electric potentials within the brain during the behavioral tasks, thereby studying the spatiotemporal dynamics of neuronal currents throughout the brain. Similarly we deploy machine learning algorithms to assess and measure the predictability of these behavioral actions by identifying action-relevant spatio-temporal patterns in the neural activity in sensor (EEG) and/or source spaces.&#160;Integrating traditional ERD/ERS, source and decoding analysis provide a more comprehensive developmental description of the neural basis of such behaviors.
This setup allowed us to exploit the advantages of the MoBI approach 2,3 and study the social interactions between the infant and the experimenter as they naturally occur without restrictions.
The protocol, from the time the subject arrives to the time he/she leaves, takes approximately 1 hr to complete. The IMU/EEG setup time and electrode location acquisition varies from 15 - 25 min depending on factors such as hair length and cooperation of the subject. The initialization and configuration of the equipment adds up to 10 min, and the testing session lasts approximately 15 min. Removal of the IMUs and EEG cap, including cleaning the head of the infant from the hypoallergenic gel, takes 5-10 min.

<table border="0">
	<tbody>
		<tr>
			<td>BrainAmp Amplifier</td>
			<td>Brain Products, Gmbh</td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>actiCAP EEG cap- 64 electrodes*</td>
			<td>Brain Products, Gmbh</td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>actiCAP Control Box</td>
			<td>Brain Products, Gmbh</td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>Brain Vision Recorder software v1.20.0601</td>
			<td>Brain Products, Gmbh</td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>actiCAP Control Box software v1.2.5.2</td>
			<td>Brain Products, Gmbh</td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>CapTrak</td>
			<td>Brain Products, Gmbh</td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>CapTrak software v1.0.0</td>
			<td>Brain Products, Gmbh</td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>Opal movement monitor</td>
			<td>APDM, Inc</td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>Opal docking station</td>
			<td>APDM, Inc</td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>Opal wireless access point</td>
			<td>APDM, Inc</td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>Motion Studio software v1.0.0.201503302222</td>
			<td>APDM, Inc</td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>Trigger box</td>
			<td>Custom</td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>Video camera</td>
			<td>HC-W850M, Panasonic Co.</td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td colspan="3">*The EEG caps come in the following head circumference sizes for infants: 42, 44, 46, 48, 50cm. For this protocol, a stock of 2 caps of each size is recommended.&#160;</td>
			<td></td>
			<td></td>
			<td></td>
		</tr>
	</tbody>
</table>

a novel experimental and analytical approach to the multimodal neural decoding of intent during social interaction in freely-behaving human infants

Understanding typical and atypical development remains one of the fundamental questions in developmental human neuroscience. Traditionally, experimental paradigms and analysis tools have been limited to constrained laboratory tasks and contexts due to technical limitations imposed by the available set of measuring and analysis techniques and the age of the subjects. These limitations severely limit the study of developmental neural dynamics and associated neural networks engaged in cognition, perception and action in infants performing &#8220;in action and in context&#8221;. This protocol presents a novel approach to study infants and young children as they freely organize their own behavior, and its consequences in a complex, partly unpredictable and highly dynamic environment. The proposed methodology integrates synchronized high-density active scalp electroencephalography (EEG), inertial measurement units (IMUs), video recording and behavioral analysis to capture brain activity and movement non-invasively in freely-behaving infants. This setup allows for the study of neural network dynamics in the developing brain, in action and context, as these networks are recruited during goal-oriented, exploration and social interaction tasks.

Figure 8 displays sample EEG and acceleration data from the IMUs for a 16 sec time window recorded during interaction between the subject and the experimenter. EEG data was re-sampled at 100 Hz and then band-pass filtered [1-40 Hz] using a 3rd order, zero-phase Butterworth filter. Channels with high impedance values (Z &#62; 60 k&#937;), and peripheral channels, were discarded 12,15. The IMUs recorded nine signals at 128 Hz: magnetic flux, angular velocity, and linear acceleration in the three Cartesian axes. Here we show the magnitude of the gravity-compensated (GC) acceleration. The effect of acceleration due to gravity was compensated by applying a Kalman filter to predict the IMU orientation in a global frame 16. Data was segmented by visual inspection of the video recordings (Step 7.1). Vertical solid lines indicate the start of a behavior of interest, as dotted vertical lines represent the ending of the event.
Influence of motion artifacts is present in the EEG data shown in Figure 8 around 709s. The unconstrained approach to data collection in this experimental protocol makes the EEG data susceptible to eye blinks, eye movements, motion and electromyographic artifacts. The data was preprocessed by using a 3rd order zero-phase Butterworth band-pass filter to constrain it to the delta-band (1-4Hz), and standardized by subtracting the mean and dividing by the standard deviation. High-amplitude artifacts were removed automatically using the Artifact Subspace Removal (ASR) method 17. Additionally, peripheral channels are excluded from the data analysis in an effort to minimize myoelectric artifact contamination. Frontalis and temporalis muscle contractions merge with EEG signal most prominently at peripheral locations: Frontalis contraction shows up in anterior locations, and temporalis contraction shows up in lateral frontal and temporal locations 15.
To inspect the nature of the data collected with this protocol, EEG data histograms were plotted in Figure 9. In&#160;Figure 9A, it describes the data distribution of the standardized signal from three spatially representative electrodes. The EEG data shows a multimodal distribution for the analyzed behaviors. In Figure 9B&#160;the kurtosis values are presented as bar graphs for easier visual inspection of the data.
Classification was performed by extracting time-based lags of each EEG channel, reducing dimensionality while preserving the local scatter of each class (Local Fisher&#8217;s discriminant analysis (LFDA)) 18, and training/testing a model of the reduced set of features (Gaussian Mixture Models (GMMs)) 19. Training/testing samples were randomly sampled over 20 iterations (i.e., cross-validation) to prevent any over-fitting.Training/testing sample size varies given the number of rejected channels (i.e., impedance greater than 60 kHz), length of experiment session, and number of trials and behaviors expressed. However, the number of training and testing samples used for each class (behavior) correspond to 50% of the least populated class. As an example, the testing set size of each class is N = 1,069 samples for the infant data shown in Figure 10. All pre-processing and classification steps were computed under the MATLAB programming environment.
Figure 11 depicts the whole procedure for EEG source estimation performed in this study in a step-by-step manner. More details about each step is also summarized in section 8.
Figure 12 shows results of event related (de) synchronization (ERD / ERS) in the mu rhythm (5-9 Hz) and the dipole sources during the &#8220;Reach-Offer&#8221; task. ERS and ERD was calculated as the percentage of a decrease or increase in a frequency-band power which occurs during event (reach and offer task) interval as compared to the reference interval (a segment taken prior to the event). This figure also shows gravity-compensated magnitude acceleration obtained during the task from both wrists of the infant and the actor. For a dipole analysis the EEG signals were decomposed using independent component analysis (ICA) to eliminate background noise. The source estimation was performed in mu rhythm after ICA preprocessing via a fixed MUSIC algorithm 5. As expected, the sources were localized over the right primary motor area while the subject was using his left hand to grasp the object.
<table border="0" cellpadding="0" cellspacing="0">
	<tbody>
		<tr>
			<td>Product Name</td>
			<td>Company</td>
			<td>Quantity</td>
		</tr>
		<tr>
			<td>BrainAmp Amplifier</td>
			<td>Brain Products, Gmbh</td>
			<td>2</td>
		</tr>
		<tr>
			<td>actiCAP EEG cap- 64 electrodes*</td>
			<td>Brain Products, Gmbh</td>
			<td>10</td>
		</tr>
		<tr>
			<td>actiCAP Control Box</td>
			<td>Brain Products, Gmbh</td>
			<td>1</td>
		</tr>
		<tr>
			<td>Brain Vision Recorder software</td>
			<td>Brain Products, Gmbh</td>
			<td>1</td>
		</tr>
		<tr>
			<td>actiCAP Control Box software</td>
			<td>Brain Products, Gmbh</td>
			<td>1</td>
		</tr>
		<tr>
			<td>CapTrak</td>
			<td>Brain Products, Gmbh</td>
			<td>1</td>
		</tr>
		<tr>
			<td>CapTrak software</td>
			<td>Brain Products, Gmbh</td>
			<td>1</td>
		</tr>
		<tr>
			<td>Opal movement monitor</td>
			<td>APDM, Inc</td>
			<td>6</td>
		</tr>
		<tr>
			<td>Opal docking station</td>
			<td>APDM, Inc</td>
			<td>6</td>
		</tr>
		<tr>
			<td>Opal wireless access point</td>
			<td>APDM, Inc</td>
			<td>1</td>
		</tr>
		<tr>
			<td>Motion Studio software</td>
			<td>APDM, Inc</td>
			<td>1</td>
		</tr>
		<tr>
			<td>Trigger box</td>
			<td>Custom</td>
			<td>1</td>
		</tr>
		<tr>
			<td>Video camera</td>
			<td>HC-W850M, Panasonic Co.</td>
			<td>1</td>
		</tr>
		<tr>
			<td colspan="3">*The EEG caps come in the following head circumference sizes for infants: 42, 44, 46, 48, 50 cm. For this protocol, a stock of 2 caps of each size is recommended.&#160;</td>
		</tr>
	</tbody>
</table>
Table 1 Equipment.
<img alt="Figure 1" src="/files/ftp_upload/53406/53406fig1.jpg" /> 
Figure 1. Flow diagrams. (A) Flow diagram of the experiment setup.&#160;(B) Flow diagram of the data collection session. <a href="https://www.jove.com/files/ftp_upload/53406/53406fig1large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 2" src="/files/ftp_upload/53406/53406fig2.jpg" /> 
Figure 2. Subject setup and ready for data collection.&#160;(A) Frontal view of subject wearing the EEG cap, and four IMUs. (B) Topographic representation of the 64 channel EEG cap using the 10-20 standard electrode positioning system. <a href="https://www.jove.com/files/ftp_upload/53406/53406fig2large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 3" src="/files/ftp_upload/53406/53406fig3.jpg" /> 
Figure 3. Data collection equipment and the testing room. (A) Data collection equipment setup: EEG control box (1), EEG cap (2), trigger (3), wireless IMU data receiver (4), EEG amplifiers (5), toy box (6), trigger push-button (7), data acquisition laptops (8), IMUs docking station (9). (B) Close-up of EEG cap, active electrodes and control box. (C) Close-up of IMUs and wireless receiver, trigger push-button, EEG amplifiers, and data collection laptops. <a href="https://www.jove.com/files/ftp_upload/53406/53406fig3large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 4" src="/files/ftp_upload/53406/53406fig4.jpg" /> 
Figure 4. Schematic representation of experimental setting. EEG control box holder (10). Subject&#8217;s guardian sits on chair (11), experimenter sits on chair (14) and the person monitoring the data sits on chair (17). The rest of the experimental setup is shown: Interaction tabletop (12), trigger (13), toy box (15), data acquisition table (16), video camera placement (18). <a href="https://www.jove.com/files/ftp_upload/53406/53406fig4large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 5" src="/files/ftp_upload/53406/53406fig5.jpg" /> 
Figure 5. Screenshot of the EEG control software. The arrows point to key icons in the software described in step 6.1 and 6.8. <a href="https://www.jove.com/files/ftp_upload/53406/53406fig5large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 6" src="/files/ftp_upload/53406/53406fig6.jpg" /> 
Figure 6. Screenshot of EEG recording software. The arrows point to key icons in the software described in step 6.2&#160;and 6.3. A section of the live-streaming raw data is shown. <a href="https://www.jove.com/files/ftp_upload/53406/53406fig6large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 7" src="/files/ftp_upload/53406/53406fig7.jpg" /> 
Figure 7. Screenshot of the IMUs software. The red boxes highlight the key icons in the software described in step 6.4. (A) Main command window. (B) Configuration window. (C) Recording window. <a href="https://www.jove.com/files/ftp_upload/53406/53406fig7large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 8" src="/files/ftp_upload/53406/53406fig8.jpg" /> 
Figure 8. Sample data from a representative experimental session. (A) EEG and acceleration data are shown, both band-pass filtered [1-40Hz]. EEG electrodes with low impedance (Z &#60; 60 k&#937;) throughout the session are displayed. Solid vertical lines indicate behavior onset. (B) Still frame representations of the infant engaged in the behaviors analyzed: Observe, Reach-grasp, Reach-offer, Imitate, Explore, Rest. <a href="https://www.jove.com/files/ftp_upload/53406/53406fig8large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 9" src="/files/ftp_upload/53406/53406fig9.jpg" /> 
Figure 9. Sample histograms of EEG amplitude for six behaviors identified. A) The data shown corresponds to the raw data from three electrodes (CP2, FC5, POz) recorded in a session with a 20-month old infant. The histograms are scaled to the highest number of occurrences and the kurtosis of each histogram is shown on the right of each plot. Notice the multimodal distribution for most of the behaviors. B) Kurtosis of sample data for each behavior. <a href="https://www.jove.com/files/ftp_upload/53406/53406fig9large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 10" src="/files/ftp_upload/53406/53406fig10.jpg" /> 
Figure 10. EEG predicts behavioral actions in freely behaving infants 12. A sample confusion matrix for the classification accuracy for a 20 month old infant is shown. The overall decoding accuracy is displayed on the lower-right corner. <a href="https://www.jove.com/files/ftp_upload/53406/53406fig10large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 11" src="/files/ftp_upload/53406/53406fig11.jpg" /> 
Figure 11. Source Localization. Steps required for an infant dipole source localization using an anatomical MRI and a functional EEG data. <a href="https://www.jove.com/files/ftp_upload/53406/53406fig11large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 12 " src="/files/ftp_upload/53406/53406fig12.jpg" /> 
Figure 12. Reach-offer task analysis profile.A) Snapshots of an infant performing the &#8220;Reach-Offer&#8221; task. B) Mean Global Field Power of EEG in Mu band (5-9 Hz), the limb movement trajectories of the infant and the actor; the grand averaged event related spectral perturbation from all channels. C) Localized dipole sources for two events of &#8220;Movement onset&#8221; and &#8220;Movement Completion&#8221;. <a href="https://www.jove.com/files/ftp_upload/53406/53406fig12large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>

Watch this Scientific Journal Video about A Novel Experimental and Analytical Approach to the Multimodal Neural Decoding of Intent During Social Interaction in Freely-behaving Human Infants at JoVE.co

A Novel Experimental and Analytical Approach to the Multimodal Neural Decoding of Intent During Social Interaction in Freely-behaving Human Infants

This protocol presents a novel methodology for the neural decoding of intent from freely-behaving infants during unscripted social interaction with an actor. Neural activity is acquired using non-invasive high-density active scalp electroencephalography (EEG). Kinematic data is collected with inertial measurement units and supplemented with synchronized video recording.

Understanding typical and atypical development remains one of the fundamental questions in developmental human neuroscience. Traditionally, experimental ...

a-novel-experimental-analytical-approach-to-multimodal-neural

Research

JoVE Journal

Behavior

Long-term Potentiation of Perforant Pathway-dentate Gyrus Synapse in Freely Behaving Mice

Studies of long-term potentiation of synaptic efficacy, an activity-dependent synaptic phenomenon having properties that make it attractive as a potential cellular mechanism underlying learning and information storage, have long been used to elucidate the physiology of various neuronal circuits in the hippocampus, amygdala, and other limbic and cortical structures. With this in mind, transgenic mouse models of neurological diseases represent useful platforms to conduct long-term&#160;potentiation (LTP) studies to develop a greater understanding of the role of genes in normal and abnormal synaptic communication in neuronal networks involved in learning, emotion and information processing. This article describes methodologies for reliably inducing LTP in the freely behaving mouse. These methodologies can be used in studies of transgenic and knockout freely&#160;behaving mouse models of neurodegenerative diseases.

Transgenic and knockout mouse models of neurological diseases are useful for studying the role of genes in normal and abnormal neurophysiology. This article describes methodologies which can be used to study long-term potentiation, a cellular mechanism which may underlie learning and memory, in transgenic and knockout freely&#160;behaving mouse models of neuropathology.

Studies of long-term potentiation of synaptic efficacy, an activity-dependent synaptic phenomenon having properties that make it attractive as a potential ...

Measuring Neural and Behavioral Activity During Ongoing Computerized Social Interactions: An Examination of Event-Related Brain Potentials

Social exclusion is a complex social phenomenon with powerful negative consequences. Given the impact of social exclusion on mental and emotional health, an understanding of how perceptions of social exclusion develop over the course of a social interaction is important for advancing treatments aimed at lessening the harmful costs of being excluded. To date, most scientific examinations of social exclusion have looked at exclusion after a social interaction has been completed. While this has been very helpful in developing an understanding of what happens to a person following exclusion, it has not helped to clarify the moment-to-moment dynamics of the process of social exclusion. Accordingly, the current protocol was developed to obtain an improved understanding of social exclusion by examining the patterns of event-related brain activation that are present during social interactions. This protocol allows greater precision and sensitivity in detailing the social processes that lead people to feel as though they have been excluded from a social interaction. Importantly, the current protocol can be adapted to include research projects that vary the nature of exclusionary social interactions by altering how frequently participants are included, how long the periods of exclusion will last in each interaction, and when exclusion will take place during the social interactions. Further, the current protocol can be used to examine variables and constructs beyond those related to social exclusion. This capability to address a variety of applications across psychology by obtaining both neural and behavioral data during ongoing social interactions suggests the present protocol could be at the core of a developing area of scientific inquiry related to social interactions.

Research on social exclusion has grown tremendously in recent years. As the field expands, it is imperative to develop sophisticated methodologies allowing for the simultaneous measurement of neural and behavioral outcomes during social exclusion. This protocol utilizes event-related brain potentials to record ongoing neural activity during computerized social interactions.

Social exclusion is a complex social phenomenon with powerful negative consequences. Given the impact of social exclusion on mental and emotional health, ...

The Modified Hole Board - Measuring Behavior, Cognition and Social Interaction in Mice and Rats

This protocol describes the modified hole board (mHB), which combines features from a traditional hole board and open field and is designed to measure multiple dimensions of unconditioned behavior in small laboratory mammals (e.g., mice, rats, tree shrews and small primates). This paradigm is a valuable alternative for the use of a behavioral test battery, since a broad behavioral spectrum of an animal&#8217;s behavioral profile can be investigated in one single test.
The apparatus consists of a box, representing the &#8216;protected&#8217; area, separated from a group compartment. A board, on which small cylinders are staggered in three lines, is placed in the center of the box, representing the &#8216;unprotected&#8217; area of the set-up. The cognitive abilities of the animals can be measured by baiting some cylinders on the board and measuring the working and reference memory. Other unconditioned behavior, such as activity-related-, anxiety-related- and social behavior, can be observed using this paradigm. Behavioral flexibility and the ability to habituate to a novel environment can additionally be observed by subjecting the animals to multiple trials in the mHB, revealing insight into the animals&#8217; adaptive capacities.
Due to testing order effects in a behavioral test battery, na&#239;ve animals should be used for each individual experiment. By testing multiple behavioral dimensions in a single paradigm and thereby circumventing this issue, the number of experimental animals used is reduced. Furthermore, by avoiding social isolation during testing and without the need to food deprive the animals, the mHB represents a behavioral test system, inducing if any, very low amount of stress.

This protocol describes the modified hole board, which is a behavioral test set-up that comprises the characteristics of an open field and a traditional hole board. This set-up enables the differential analysis of unconditioned behavior of small laboratory mammals as well as the analysis of cognitive abilities.

This protocol describes the modified hole board (mHB), which combines features from a traditional hole board and open field and is designed to measure ...

Quantifying Learning in Young Infants: Tracking Leg Actions During a Discovery-learning Task

Task-specific actions emerge from spontaneous movement during infancy. It has been proposed that task-specific actions emerge through a discovery-learning process. Here a method is described in which 3-4 month old infants learn a task by discovery and their leg movements are captured to quantify the learning process. This discovery-learning task uses an infant activated mobile that rotates and plays music based on specified leg action of infants. Supine infants activate the mobile by moving their feet vertically across a virtual threshold. This paradigm is unique in that as infants independently discover that their leg actions activate the mobile, the infants&#8217; leg movements are tracked using a motion capture system allowing for the quantification of the learning process. Specifically, learning is quantified in terms of the duration of mobile activation, the position variance of the end effectors (feet) that activate the mobile, changes in hip-knee coordination patterns, and changes in hip and knee muscle torque. This information describes infant exploration and exploitation at the interplay of person and environmental constraints that support task-specific action. Subsequent research using this method can investigate how specific impairments of different populations of infants at risk for movement disorders influence the discovery-learning process for task-specific action.

A method is described in which 3-4 month old infants learn a task by discovery and their leg movements are captured to quantify the learning process.

Task-specific actions emerge from spontaneous movement during infancy. It has been proposed that task-specific actions emerge through a discovery-learning ...

The Social Dimension of Stress: Experimental Manipulations of Social Support and Social Identity in the Trier Social Stress Test

In many situations humans are influenced by the behavior of other people and their relationships with them. For example, in stressful situations supportive behavior of other people as well as positive social relationships can act as powerful resources to cope with stress. In order to study the interplay between these variables, this protocol describes two effective experimental manipulations of social relationships and supportive behavior in the laboratory. In the present article, these two manipulations are implemented in the Trier Social Stress Test (TSST)&#8212;a standard stress induction paradigm in which participants are subjected to a simulated job interview. More precisely, we propose (a) a manipulation of the relationship between different protagonists in the TSST by making a shared social identity salient and (b) a manipulation of the behavior of the TSST-selection committee, which acts either supportively or unsupportively. These two experimental manipulations are designed in a modular fashion and can be applied independently of each other but can also be combined. Moreover, these two manipulations can also be integrated into other stress protocols and into other standardized social interactions such as trust games, negotiation tasks, or other group tasks.

Previous research on the social dimension of stress has focused on two important variables: social identity and social support. This protocol introduces an effective experimental manipulation of these two social variables and describes their implementation in a standard stress induction paradigm (Trier Social Stress Test).

In many situations humans are influenced by the behavior of other people and their relationships with them. For example, in stressful situations ...

Sit-to-stand-and-walk from 120% Knee Height: A Novel Approach to Assess Dynamic Postural Control Independent of Lead-limb

Individuals with sensorimotor pathology e.g., stroke have difficulty executing the common task of rising from sitting and initiating gait (sit-to-walk: STW). Thus, in clinical rehabilitation separation of sit-to-stand and gait initiation - termed sit-to-stand-and-walk (STSW) - is usual. However, a standardized STSW protocol with a clearly defined analytical approach suitable for pathological assessment has yet to be defined.
Hence, a goal-orientated protocol is defined that is suitable for healthy and compromised individuals by requiring the rising phase to be initiated from 120% knee height with a wide base of support independent of lead limb. Optical capture of three-dimensional (3D) segmental movement trajectories, and force platforms to yield two-dimensional (2D) center-of-pressure (COP) trajectories permit tracking of the horizontal distance between COP and whole-body-center-of-mass (BCOM), the decrease of which increases positional stability but is proposed to represent poor dynamic postural control.
BCOM-COP distance is expressed with and without normalization to subjects&#39; leg length. Whilst COP-BCOM distances vary through STSW, normalized data at the key movement events of seat-off and initial toe-off (TO1) during steps 1 and 2 have low intra and inter subject variability in 5 repeated trials performed by 10 young healthy individuals. Thus, comparing COP-BCOM distance at key events during performance of an STSW paradigm between patients with upper motor neuron injury, or other compromised patient groups, and normative data in young healthy individuals is a novel methodology for evaluation of dynamic postural stability.

Here, we present a novel protocol to measure positional stability at key events during the sit-to-stand-to-walk using the center-of-pressure to the whole-body-center-of-mass distance. This was derived from the force platform and three-dimensional motion-capture technology. The paradigm is reliable and can be utilized for the assessment of neurologically compromised individuals.

Individuals with sensorimotor pathology e.g., stroke have difficulty executing the common task of rising from sitting and initiating gait (sit-to-walk: ...

A Within-subjects Experimental Protocol to Assess the Effects of Social Input on Infant EEG

Despite the importance of social interactions for infant brain development, little research has assessed functional neural activation while infants socially interact. Electroencephalography (EEG) power is an advantageous technique to assess infant functional neural activation. However, many studies record infant EEG only during one baseline condition. This protocol describes a paradigm that is designed to comprehensively assess infant EEG activity in both social and nonsocial contexts as well as tease apart how different types of social inputs differentially relate to infant EEG. The within-subjects paradigm includes four controlled conditions. In the nonsocial condition, infants view objects on computer screens. The joint attention condition involves an experimenter directing the infant's attention to pictures. The joint attention condition includes three types of social input: language, face-to-face interaction, and the presence of joint attention. Differences in infant EEG between the nonsocial and joint attention conditions could be due to any of these three types of input. Therefore, two additional conditions (one with language input while the experimenter is hidden behind a screen and one with face-to-face interaction) were included to assess the driving contextual factors in patterns of infant neural activation. Representative results demonstrate that infant EEG power varied by condition, both overall and differentially by brain region, supporting the functional nature of infant EEG power. This technique is advantageous in that it includes conditions that are clearly social or nonsocial and allows for examination of how specific types of social input relate to EEG power. This paradigm can be used to assess how individual differences in age, affect, socioeconomic status, and parent-infant interaction quality relate to the development of the social brain. Based on the demonstrated functional nature of infant EEG power, future studies should consider the role of EEG recording context and design conditions that are clearly social or nonsocial.

This novel protocol is designed to assess the neural bases of social interaction in infants. The paradigm is designed to tease apart how various social inputs such as language, joint attention, and face-to-face interaction relate to infant neural activation. Infant EEG power is recorded during both social and nonsocial conditions.

Despite the importance of social interactions for infant brain development, little research has assessed functional neural activation while infants ...

A Wireless, Bidirectional Interface for In Vivo Recording and Stimulation of Neural Activity in Freely Behaving Rats

In vivo electrophysiology is a powerful technique to investigate the relationship between brain activity and behavior at a millisecond and micrometer scale. However, current methods mostly rely on tethered cable recordings or only use unidirectional systems, allowing either recording or stimulation of neural activity, but not at the same time or same target. Here, a new wireless, bidirectional device for simultaneous multichannel recording and stimulation of neural activity in freely behaving rats is described. The system operates through a single portable head stage that both transmits recorded activity and can be targeted in real-time for brain stimulation using a telemetry-based multichannel software. The head stage is equipped with a preamplifier and a rechargeable battery, allowing stable long-term recordings or stimulation for up to 1 h. Importantly, the head stage is compact, weighs 12 g (including battery) and thus has minimal impact on the animal&#180;s behavioral repertoire, making the method applicable to a broad set of behavioral tasks. Moreover, the method has the major advantage that the effect of brain stimulation on neural activity and behavior can be measured simultaneously, providing a tool to assess the causal relationships between specific brain activation patterns and behavior. This feature makes the method particularly valuable for the field of deep brain stimulation, allowing precise assessment, monitoring, and adjustment of stimulation parameters during long-term behavioral experiments. The applicability of the system has been validated using the inferior colliculus as a model structure.

A Wireless, Bidirectional Interface for In Vivo Recording and Stimulation of Neural Activity in Freely Behaving Rats

A wireless, bidirectional system for multi-channel neural recordings and stimulation in freely behaving rats is introduced. The system is light and compact, thus having minimal impact on the animal&#180;s behavioral repertoire. Moreover, this bidirectional system provides a sophisticated tool to assess causal relationships between brain activation patterns and behavior.

In vivo electrophysiology is a powerful technique to investigate the relationship between brain activity and behavior at a millisecond and micrometer ...

A Behavioral Assay for Investigating the Role of Spatial Memory During Instinctive Defense in Mice

Evolution has selected a repertoire of defensive behaviors that are essential for survival across all animal species. These behaviors are often stereotyped actions elicited in response to innately aversive sensory stimuli, but their success requires enough flexibility for adapting to different spatial environments, which can change rapidly. Here, we describe a behavioral assay to evaluate the influence of learned spatial knowledge on defensive behaviors in mice. We have adapted the widely used Barnes maze spatial memory assay to investigate how mice navigate to a shelter during escape responses to innately aversive sensory stimuli in a novel environment, and how they adapt to acute changes in the environment. This new assay is an ethological paradigm that does not require training and exploits the natural exploration patterns and navigation strategies in mice. We propose that the set of protocols described here are a powerful means of studying goal-directed behaviors and stimulus-triggered navigation, which should be of interest to both the fields of instinctive behaviors and spatial memory.

This protocol describes a behavioral assay, based on the Barnes maze test, to study how instinctive defensive actions are modified by the knowledge of spatial environment.

Evolution has selected a repertoire of defensive behaviors that are essential for survival across all animal species. These behaviors are often ...

Enactive Phenomenological Approach to the Trier Social Stress Test: A Mixed Methods Point of View

The single Trier Social Stress Test (TSST) and the TSST for groups (TSST-G) are the most used protocols to experimentally induce psychosocial stress. These tests are based on uncontrollability and social-evaluative threat, inducing psychological and physiological consequences (e.g., anxiety, emotional states, salivary cortisol increases). Many quantitative experimental studies have investigated these stress inducers and these consequences. But, as far as we know, this study is the first to provide a qualitative analysis to access the participants' voices so as to understand the dynamics of their experience throughout the TSST and the TSST-G. This paper outlines a mixed methods approach to the TSST. This approach can help to maximize the information that can be gained from the TSST, which researchers often use without looking more closely at what is qualitatively happening psychologically for participants during the stressor itself. In this way, this protocol is an example of mixed methods, showing the added value of using the enactive phenomenological approach to analyze experimental protocols more deeply. This kind of mixed methods is helpful to access the experience, to understand the actor's point of view, and to analyze in-depth the dynamics of cognitive processes like intentions, perceptions, enacted knowledge, and emotion. The discussion section shows the different uses of a mixed methods protocol, exploiting the enactive phenomenological approach to analyze a protocol or to give a cross vision of the same research subject. This section deals with different existing applications, pointing out some critical steps in this mixed methods approach.

Here, we present an original mixed method combining a quantitative and a qualitative approach to examine accurately participants&#39; experiences during the Trier Social Stress Test and the Trier Social Stress Test for groups.

The single Trier Social Stress Test (TSST) and the TSST for groups (TSST-G) are the most used protocols to experimentally induce psychosocial stress. ...