This research was supported by: National Natural Science Foundation of China (31872783, 31800951).

The protocol presented here was approved by the University Committee on Human Research Protection of East China Normal University.
1. Preparation for the experiment
<ol>
	<li>Participants
	<ol>
		<li>Recruit a group of undergraduate and graduate students with monetary compensation by the campus advertising.</li>
		<li>Ensure that the participants are right-handed and have normal or corrected-to-normal vision and hearing. Ensure that they have not studied music or have studied it for fewer than 3 years before.</li>
		<li>Randomly match the students in dyads. To control the potential effect of partner familiarity on social coordination18, ensure that the members of each dyad have not seen or known each other before.</li>
	</ol>
	</li>
	<li>Experimental stimulus
	<ol>
		<li>Create the auditory stimuli (440 Hz, 660 ms pure tones) by any free music composition and notation software.</li>
		<li>Repeat the tones with an interval of 500-1000 ms and combine them into a tone sequence. Each tone sequence is 12 s longer and composed of 12 tones.</li>
		<li>For one tone sequence, accent every first tone (+6 dB) to create the pattern of downbeats and upbeats, defined as the meter stimulus (Supplementary Audio 1). In the second tone sequence, unaccent the tones with equal intensity (40 dB above individual sensation threshold, collected before the experiment task), which corresponded to the non-meter stimulus (Supplementary Audio 2).</li>
	</ol>
	</li>
	<li>Experimental task
	<ol>
		<li>Program the experimental task by using a psychological software tool.</li>
		<li>Arrange two stages for the experimental task (Figure 1A) as described in steps 1.3.3-1.3.6.</li>
		<li>second resting state: Ask participants to remain as motionless as possible with their minds relaxed and eyes closed.</li>
		<li>Finger tapping task: Request the participants to complete two parts: a coordination part and an independence part.</li>
		<li>During the coordination part, provide auditory feedback (i.e., a drip sound corresponding to one tap) to each participant only for the response generated by the other member of the dyad. Ask the participants to try their best to respond synchronously with the other member.</li>
		<li>For the independence part, ensure both participants received the auditory feedback (i.e., a drip sound corresponding to one tap) of their own responses and ask them to respond synchronously with the auditory stimulus as precisely as possible. 
		NOTE: Combined with the meter and non-meter stimuli, participants found themselves in one of four different conditions: (i) meter coordination - both participants heard meters, and the responses from each other; (ii) non-meter coordination - both participants heard non-meters and the responses from each other; (iii) meter independence - both participants heard meters and the responses from themselves; (iv) non-meter independence - both participants heard non-meters and the responses from themselves.</li>
		<li>For each trial, let the participants first hear a piece of the auditory stimulus (12 s) followed by a sound (262 Hz, 1000 ms) that serves as a cue to start to tap the finger.</li>
		<li>Ask the participants to reproduce the stimulus they heard before by tapping their right index finger on the keyboard (participant #1: &#34;f&#34;; participant #2: &#34;j&#34;). Participants must tap 12 times while keeping the same time interval between tones as the previously presented stimulus. 
		NOTE: There were 60 trials assigned equally in 4 blocks corresponding to the 4 experimental conditions, namely 15 trials in one block. The order of the blocks was counterbalanced. The total duration of the tapping task was about 26 min.</li>
		<li>Between blocks, let the participants rest for 30 s.</li>
		<li>During the whole experiment, do not allow the participants to communicate through any language or movement. Separate the participants with the computer monitor to block any visual information that might deliver messages between them.</li>
	</ol>
	</li>
	<li>Homemade fNIRS caps: Buy two elastic swimming caps of normal size. To cover the brain region of interest, mend the swimming caps as described in the following steps:
	<ol>
		<li>Put one swimming cap on a headform, and then put a standard 10-20 EEG cap on the swimming cap.</li>
		<li>Mark the location of FCz on the swimming cap with a red magic marker.</li>
		<li>Take off the EEG cap from the headform.</li>
		<li>Put one optode probe patch (3 x 5 setup) on the swimming cap, aligning the middle one of the second probe row of the patch with the marked location of FCz. 
		NOTE: The optode probe patch included 15 locations of optode probes (i.e., 8 emitters and 7 detectors), forming 22 measurement channels with 3 cm optode separation (Figure 1B).</li>
		<li>Mark the locations of the 15 probes of the patch on the swimming cap.</li>
		<li>Take off the patch and the swimming cap from the headform.</li>
		<li>Cut 15 small holes on the marked locations of the 15 probes with a pair of scissors.</li>
		<li>Mount the patch to the modified swimming cap by embedding the locations of 15 probes into the appropriate 15 holes.</li>
		<li>Mend the other swimming cap according to the above process. 
		NOTE: The Homemade fNIRS cap, in which the locations of optodes are as per the standard EEG locations, were employed as there were no applicable standard EEG caps for the fNIRS system used in this study. There is no need to make an fNIRS cap if there are suitable standard EEG caps with the fNIRS system.</li>
	</ol>
	</li>
</ol>
2. Before participants arrive
NOTE: Ensure to follow steps 2.1-2.5 before participants arrive at the laboratory.
<ol>
	<li>Remind the two participants of one dyad to come to the laboratory per the agreed time schedule.</li>
	<li>Start the fNIRS system at least 30 min in advance, leaving the laser turned off.</li>
	<li>Insert the optode probes from the fNIRS system into the optode probe patches.</li>
	<li>Examine the parameters of fNIRS measurement (i.e., subject ID, the event-related mode, the optode probe arrangement).</li>
	<li>Set the experimental apparatus with one table, two chairs, two 19-in computer monitors, and two pairs of headphones (Figure 1C).</li>
</ol>
3. Participant arrival in the laboratory
NOTE: Sincerely appreciate the two participants of one dyad when they arrive at the fNIRS lab. Request them to put their phone on silent mode and temporarily leave their personal belongings in the cabinet. Then conduct the following processes in sequence:
<ol>
	<li>Before the participants sit down, reconfirm that the two participants have not seen each other before. Ensure that they did not communicate with each other through any language or movement while in the lab.</li>
	<li>Provide the participants with informed consent forms approved by the University Committee on Human Research.</li>
	<li>Instruct the participants on the details of the experimental task. Ask them to wear headphones and give them several practicing trials.</li>
	<li>In the practice trials, allow the two participants of each dyad to practice together. 
	NOTE: Before wearing the fNIRS cap, it is worthwhile to measure and determine the head size for each participant by using a flexible rule. Then select the right size cap for the participant according to his/her head size. In this study, such a step was missed as one-size swimming caps were used. It is better to conduct this step on the day before the experiment because the relative operations (i.e., measuring head size, selecting the right size swimming cap, mounting the optode probe patches to the swimming cap, and inserting the optode probes into the probe patches) are time-consuming (about 20-30 min).</li>
	<li>Put the fNIRS cap on the head of participants with the center of the cap pointing at the location of CZ, and place the middle optode probe of the second probe row of the patch at FCz.</li>
	<li>Operate the fNIRS system to perform the signal calibration with the laser turned on.</li>
	<li>If there is an insufficient signal at some channel, adjust the signal intensity with a fiber stick to gently put the hair underneath the surrounding probe tip aside.</li>
	<li>If necessary, press the probes gently but ensure not to hurt the participants.</li>
	<li>Repeat steps 3.5-3.8 until the quality of the signal is accessible. Make sure that participants feel comfortable during the whole process of signal calibration.</li>
	<li>Help the participants to find a comfortable posture for themselves (e.g., comfortable body positions). Remind the participants to keep their heads as motionless as possible during the whole experimental task (i.e., about 26 min).</li>
	<li>Examine the quality of NIRS signals again. If there are sufficient signals in all channels, run the experiment procedure on the desktop computer.</li>
	<li>Help the participants take off the headphones and fNIRS cap on completion of the experimental procedure. Return their personal belongings and thank them with monetary compensation.</li>
	<li>Operate the fNIRS system to save data. Use a disc to export raw fNIRS data (.csv) and use a USB to copy the behavioral data from the computer.</li>
	<li>Close the fNIRS system and the computer if no more experimental arrangement.</li>
	<li>Keep the lab notebook ready to note down any events, especially abnormalities during the whole experiment.</li>
</ol>
4. Data analysis
NOTE: Perform all data analysis by using MATLAB software, with the following toolboxes: HOMER219, Hitachi2nirs20, xjView21, Cross Wavelet and Wavelet Coherence toolbox22, and Groppe&#39;s scripts in MathWork23.
<ol>
	<li>Data preprocessing
	<ol>
		<li>To check the data quality, follow steps 4.1.2-4.1.3</li>
		<li>Read the data files (.csv) for each participant with the readHitachData function of xjView. 
		NOTE: In this way, the Hitachi measurement data (csv format) is converted into oxyHb/deoxyHb/marker data with the information saved in the measurement (i.e., wavelength, timedata, and channel list).</li>
		<li>Visually check the quality in oxyHb and deoxyHb values by plotting all channels&#39; time series in one figure, with the function plotTraces of xjView. 
		NOTE: It is easy to identify abnormalities in the data. The channel that has much noise can be excluded in subsequent analysis.</li>
		<li>Convert Hitachi files (.csv) to .nirs files format with the csv2nirs function of Hitachi2nirs, which supports further data preprocessing with Homer2.</li>
		<li>Transform the raw data to optical density with the function hmrIntensity2OD of Homer2.</li>
		<li>Use principal component analyses (PCA)24 to remove the fNIRS global physiological noise by using the function enPCAFilter (nSV = 0.8, that is 80% of the covariance of the data was removed) of Homer2.</li>
		<li>Use correlation-based signal improvement method (CBSI)25 to remove head motion artifacts using the function hmrMotionCorrect_Cbsi of Homer2.</li>
		<li>Use modified Beer-Lembert law to transform the processed optical density into oxyHb and deoxyHb values with the hmrOD2Conc function of Homer2.</li>
	</ol>
	</li>
	<li>Calculating IBS 
	NOTE: For the preprocessed oxyHb values, use WTC to calculate the coherence values for the channel pair that are from the same location of the dyad, including the following pipeline:
	<ol>
		<li>Adopt the wtc function of Cross Wavelet and Wavelet Coherence toolbox with default parameters to compute the coherence values at each time and frequency point to obtain a two-axis matrix of coherence values.</li>
		<li>For the default parameters, use morlet mother wavelet, to transform each time series into the time and frequency domain by the continuous wavelet transformation.</li>
		<li>Select MonteCarloCount to represent the number of surrogate data sets in the significance calculation, and select Auto AR1 to calculate the autocorrelation coefficients of the time series.</li>
		<li>Choose frequency band of interest (FOI) as mentioned in steps 4.2.5-4.2.8.</li>
		<li>Select and average the coherence values of the frequency band between 0.5-1 Hz (respectively corresponding to period 2 s and 1 s), according to the frequency band used in the finger-motion task of a previous fNIRS hyperscanning study9. Such FOI also corresponded to the period of one tap in the experimental task. Thus, obtain one column of coherence values for each pair. 
		NOTE: To further statistically confirm FOI, calculate the coherence values for each dyad across the full frequency range (i.e., 0.008-10 Hz for the data), rather than just confining the selected frequency band (i.e., 0.5-1 Hz).</li>
		<li>Average the coherence values of the targeted time windows (same as 4.2.3) for each frequency point.</li>
		<li>Next, analyze the average coherence values following the pipeline described in steps 4.2.9-4.2.11 and subsequent statistics (i.e., 4.3.1 - 4.3.2) for each frequency point.</li>
		<li>Last, visually inspect FOI by plotting the statistical z values of each channel across frequency.</li>
		<li>Select and average the coherence values of the time window during the resting state (time window for 20 s-resting-state) and each experimental condition (i.e., meter coordination, non-meter coordination, meter independence, and non-meter independence), respectively, using the information of mark. Thus, obtain five coherence values for each dyad.</li>
		<li>For the task session, only select the duration during which participants tapped to reproduce the auditory stimulus, about 12 s for each trial, thus total 180 s (i.e., 12 s x 15 trials) for each experimental condition. 
		NOTE: IBS was calculated as coherence increase (the larger subtracted coherence values than zero), namely the larger coherence values in the task session compared to those in the resting-state session.</li>
		<li>Subtract the resting coherence value from the task-related coherence value, respectively, in which the coherence value during the resting-state was used as a baseline in this experiment. 
		NOTE: By repeating the above steps (4.2.1-4.2.11) across channels (i.e., 22 channels) and dyads (i.e., 16 dyads), the subtracted coherence values for each dyad at each channel were obtained finally.</li>
	</ol>
	</li>
	<li>Statistics
	<ol>
		<li>Compare the subtracted coherence values with zero at each channel for each experimental condition, using the paired samples permutation t-test with the mult_comp_perm_t1 function of Groppe&#39;s work (5000 permutations to estimate the distribution of the null hypothesis; desired family-wise alpha level- 0.05; two-tailed test, which means the alternative hypothesis is that the mean of the data is different from 0) as abnormal data distribution and limited sample size in the current experiment26. 
		NOTE: The paired samples permutation t-test here is similar to paired t-test, but the latter assumes that the data is normally distributed, whereas the former does not. Such test begins the same way as paired t-test, that is, by computing a t score (i.e., real t score) for the coherence values in different groups (one is the subtracted coherence values in the task condition, the other is zeros). Then, a permutation is generated by exchanging the coherence values of different groups, and a new t score is calculated for the subtracted coherence values and zeros following this permutation. Such permutation is conducted 5000 times. Thus, 5000 t scores are obtained. In the distribution of the 5000 t scores, the relative location of the real t score generates the p-value for the subtracted coherence values.</li>
		<li>Correct the p values (i.e., due to the multiple comparison problem, and generate from the comparisons across 22 channels in one patch) by False Discovery Rate method (p &#60; 0.05)27. Perform this correction via the mafdr function of MATLAB toolbox. 
		NOTE: If the p-value at any channel was significant (i.e., p &#60; 0.05) after FDR correction, there is IBS at that channel.</li>
		<li>Compare the coherence values between different task conditions at the channel where IBS existed, using the paired samples permutation t-test with the mult_comp_perm_t1 function of Groppe&#39;s work (same parameters as mentioned in step 4.3.1). 
		NOTE: To intuitively examine the IBS during interpersonal coordination regarding meter vs. non-meter stimuli, compare the coherence values of different conditions directly (i.e., meter coordination vs. non-meter coordination; meter coordination vs. meter independence).</li>
		<li>Calculate the behavioral performance by the absolute difference between the partners&#39; response time divided by the sum of both partners&#39; responses56.</li>
		<li>Evaluate the relationship between the IBS and behavioral performance through the permutation test based on Pearson linear correlation analysis (i.e., the&#160;mult_comp_perm_corr&#160;function of Groppe&#39;s work).</li>
	</ol>
	</li>
	<li>Validating IBS 
	NOTE: To exclude the explanations that similar stimuli, motions, or conditions induced the demonstrated IBS, use a permutation test as a validation approach, with three permutations (i.e., within the dyad, between dyad, and between condition permutations), included the followings:
	<ol>
		<li>Randomize the label of trials in the meter coordination condition (i.e., within dyad permutation, such as trial #1 and trial #13 in dyad #1) for one dyad at each channel via the randperm function of MATLAB.</li>
		<li>Follow the above pipeline of calculating IBS and statistics (i.e., sections 4.2 and 4.3, but excluding the sensitivity analysis for FOI) for the randomized trial label. 
		NOTE: Calculate coherence values of the fake pair for each condition separately, and compute coherence increase for the fake pair (i.e., subtract the resting coherence value from the task-related coherence value for the fake pair).</li>
		<li>Conduct the permutation 1000 times, followed by the pipeline of calculating IBS and statistics (sections 4.2 and 4.3).</li>
		<li>Plot the distribution of statistical z values generated within dyad permutation.</li>
		<li>Conduct steps 4.4.2-4.4.4 by randomizing pairing of the participants of the same trial in the meter coordination condition (i.e., between dyad permutation, such as participant #1 in dyad #1 and participant #1 in dyad #3).</li>
		<li>Conduct steps 4.4.2-4.4.4 by randomizing the label of conditions for the same members of one dyad in the same trial (i.e., between condition permutation, such as participant #1 in the meter coordination condition and participant #2 in the meter independence condition).</li>
	</ol>
	</li>
</ol>

The authors have nothing to disclose.

This protocol provides a step-by-step procedure to calculate and validate IBS, using the fNIRS hyperscanning approach to simultaneously collect two participants&#39; brain signals. Some critical issues involved in fNIRS data preprocessing, IBS calculation, statistics, and IBS validation are discussed below.
Data Preprocessing 
It is necessary to preprocess fNIRS data in hyperscanning studies to extract real signals from the possible noise (i.e., motion artifacts, systemic...

When people coordinate with others, their brains and bodies become a coupled unit through continuous mutual adaption. The coupling between brains can be represented by inter-brain synchronization (IBS) through the hyperscanning approach, which simultaneously records two or more individuals&#39; brain signals1. Indeed, a growing body of fNIRS/EEG hyperscanning studies has found IBS in various collaboration contexts, including finger tapping2, group walking3, playing drums4, guitar playing5, and singing/humming6. fNIRS is widely used for the research of IBS during social interaction, as it less restricts head/body motions in relatively natural settings (compared to fMRI/EEG)7.
The article presents a protocol for calculating IBS via wavelet transform coherence (WTC) method in an fNIRS hyperscanning study. WTC is a method for assessing the cross-correlation between two movement signals on the time-frequency plane and, therefore, can give more information than the traditional correlation analysis (e.g., Pearson correlation and cross-correlation), which is only in the time domain8. In addition, hemodynamic signals are transformed into wavelet components, which can effectively remove the low-frequency noise. Although WTC is time-consuming, it has been the most commonly used method of calculating IBS in action imitation9, cooperative behavior10, verbal communication11, decision making12, and interactive learning13.
The article also presents how to validate IBS with the permutation-based random paring of trials, conditions, and participants. The IBS in hyperscanning studies is always proposed to track online social interaction between individuals, while it can also be interpreted by other explanations, such as the stimulus similarity, motion similarity, or condition similarity14. Permutation test, also called randomization test, can be leveraged to test the above-mentioned null hypotheses through resampling the observed data15. By using permutation, it is useful to investigate whether the identified IBS is specific to interactive behavior, ranging from modulation of IBS within dyads to between groups of partners16.
The protocol described here details how to obtain brain signals via fNIRS technology, calculate IBS through the WTC method, and validate IBS by permutation testing in a hyperscanning study. This study aims to examine whether privileged IBS is elicited by music meters during social coordination. The brain signals were recorded in the frontal cortex, based on the location of the IBS in a previous finding1. The experimental task was originally developed by Konvalinka and her colleges17, in which participants were asked to tap their fingers with the auditory feedback from the partner or themselves after listening to the meter or non-meter stimuli.

<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td>Computer</td>
			<td>Hewlett-Packard Development Company, L.P.</td>
			<td>HP S01-pF157mcn</td>
			<td></td>
		</tr>
		<tr>
			<td>Earphone</td>
			<td>Royal Philips Electronics, Eindhoven, The Netherlands</td>
			<td>SHE2405BK/00</td>
			<td></td>
		</tr>
		<tr>
			<td>EEG cap</td>
			<td>Compumedics Neuroscan, Charlotte, USA</td>
			<td>64-channel Quik-Cap</td>
			<td></td>
		</tr>
		<tr>
			<td>E-Prime software</td>
			<td>Psychology Software Tools, Inc., Pittsburgh, USA</td>
			<td>E-Prime 3</td>
			<td></td>
		</tr>
		<tr>
			<td>fNIRS system</td>
			<td>Hitachi Medical Corporation, Tokyo, Japan</td>
			<td>ETG-7100 Optical Topography System</td>
			<td></td>
		</tr>
		<tr>
			<td>MATLAB 2014b</td>
			<td>The MathWorks, Inc., Natick, MA</td>
			<td>MATLAB 2014b</td>
			<td></td>
		</tr>
		<tr>
			<td>MuseScore</td>
			<td>Musescore Company, Belgium</td>
			<td>MuseScore 3.6.2.548021803</td>
			<td></td>
		</tr>
		<tr>
			<td>Swimming cap</td>
			<td>Decathlon Group, Villeneuve-d&#39;Ascq, France</td>
			<td>1681552</td>
			<td></td>
		</tr>
	</tbody>
</table>

how to calculate and validate inter-brain synchronization in a fnirs hyperscanning study

The dynamics between coupled brains of individuals have been increasingly represented by inter-brain synchronization (IBS) when they coordinate with each other, mostly using simultaneous-recording signals of brains (namely hyperscanning) with fNIRS. In fNIRS hyperscanning studies, IBS has been commonly assessed through the wavelet transform coherence (WTC) method because of its advantage on expanding time series into time-frequency space where oscillations can be seen in a highly intuitive way. The observed IBS can be further validated via the permutation-based random pairing of the trial, partner, and condition. Here, a protocol is presented to describe how to obtain brain signals via fNIRS technology, calculate IBS through the WTC method, and validate IBS by permutation in a hyperscanning study. Further, we discuss the critical issues when using the above methods, including the choice of fNIRS signals, methods of data preprocessing, and optional parameters of computations.&#160;In summary, using the WTC method and permutation is a potentially standard pipeline for analyzing IBS in fNIRS hyperscanning studies, contributing to both the reproducibility and reliability of IBS.

The results showed that there was IBS at channel 5 in the meter coordination condition, whereas no IBS existed in other conditions (i.e., meter independence, non-meter coordination, non-meter independence; Figure 2A). At channel 5, the IBS in the meter coordination condition was significantly higher than the coherence values in the non-meter coordination and meter independence condition (Figure 2B). Channel 5 approximately belonged to the left dorsolateral prefr...

Watch this Scientific Journal Video about How to Calculate and Validate Inter-brain Synchronization in a fNIRS Hyperscanning Study at JoVE.com

How to Calculate and Validate Inter-brain Synchronization in a fNIRS Hyperscanning Study

The dynamics between coupled brains of individuals have been increasingly represented by inter-brain synchronization (IBS) when they coordinate with each ...

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5.5K Views.  East China Normal University. The dynamics between coupled brains of individuals have been increasingly represented by inter-brain synchronization (IBS) when they coordinate with each other, mostly using simultaneous-recording signals of brains (namely hyperscanning) with fNIRS. In fNIRS hyperscanning studies, IBS has been commonly assessed through the wavelet transform coherence (WTC) method because of its advantage on expanding time series into time-frequency space where oscillations can be seen in ...

Video: How to Calculate and Validate Inter-brain Synchronization in a fNIRS Hyperscanning Study

How to Build a Laser Speckle Contrast Imaging (LSCI) System to Monitor Blood Flow

Laser Speckle Contrast Imaging (LSCI) is a simple yet powerful technique that is used for full-field imaging of blood flow. The technique analyzes fluctuations in a dynamic speckle pattern to detect the movement of particles similar to how laser Doppler analyzes frequency shifts to determine particle speed. Because it can be used to monitor the movement of red blood cells, LSCI has become a popular tool for measuring blood flow in tissues such as the retina, skin, and brain. It has become especially useful in neuroscience where blood flow changes during physiological events like functional activation, stroke, and spreading depolarization can be quantified. LSCI is also attractive because it provides excellent spatial and temporal resolution while using inexpensive instrumentation that can easily be combined with other imaging modalities. Here we show how to build a LSCI setup and demonstrate its ability to monitor blood flow changes in the brain during an animal experiment.

How to Build a Laser Speckle Contrast Imaging LSCI System to Monitor Blood Flow

This video demonstrates how to build a Laser Speckle Contrast Imaging (LSCI) system that can easily be used to monitor blood flow.

Laser Speckle Contrast Imaging (LSCI) is a simple yet powerful technique that is used for full-field imaging of blood flow.  The technique analyzes ...

How to Create and Use Binocular Rivalry

Each of our eyes normally sees a slightly different image of the world around us. The brain can combine these two images into a single coherent representation. However, when the eyes are presented with images that are sufficiently different from each other, an interesting thing happens: Rather than fusing the two images into a combined conscious percept, what transpires is a pattern of perceptual alternations where one image dominates awareness while the other is suppressed; dominance alternates between the two images, typically every few seconds. This perceptual phenomenon is known as binocular rivalry. Binocular rivalry is considered useful for studying perceptual selection and awareness in both human and animal models, because unchanging visual input to each eye leads to alternations in visual awareness and perception. To create a binocular rivalry stimulus, all that is necessary is to present each eye with a different image at the same perceived location. There are several ways of doing this, but newcomers to the field are often unsure which method would best suit their specific needs. The purpose of this article is to describe a number of inexpensive and straightforward ways to create and use binocular rivalry. We detail methods that do not require expensive specialized equipment and describe each method's advantages and disadvantages. The methods described include the use of red-blue goggles, mirror stereoscopes and prism goggles.

Binocular rivalry occurs when the eyes are presented with different images at the same location: one image dominates while the other is suppressed, and dominance alternates periodically. Rivalry is useful for investigating perceptual selection and visual awareness. Here we describe several easy methods for creating and using binocular rivalry stimuli.

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Using MazeSuite and Functional Near Infrared Spectroscopy to Study Learning in Spatial Navigation

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Functional near-infrared spectroscopy (fNIR) is an optical brain imaging technique that enables continuous, noninvasive, and portable monitoring of changes in cerebral blood oxygenation related to human brain functions2-7. Over the last decade fNIR is used to effectively monitor cognitive tasks such as attention, working memory and problem solving7-11. fNIR can be implemented in the form of a wearable and minimally intrusive device; it has the capacity to monitor brain activity in ecologically valid environments. 
Cognitive functions assessed through task performance involve patterns of brain activation of the prefrontal cortex (PFC) that vary from the initial novel task performance, after practice and during retention12. Using positron emission tomography (PET), Van Horn and colleagues found that regional cerebral blood flow was activated in the right frontal lobe during the encoding (i.e., initial na&iuml;ve performance) of spatial navigation of virtual mazes while there was little to no activation of the frontal regions after practice and during retention tests. Furthermore, the effects of contextual interference, a learning phenomenon related to organization of practice, are evident when individuals acquire multiple tasks under different practice schedules13,14. High contextual interference (random practice schedule) is created when the tasks to be learned are presented in a non-sequential, unpredictable order. Low contextual interference (blocked practice schedule) is created when the tasks to be learned are presented in a predictable order.
Our goal here is twofold: first to illustrate the experimental protocol design process and the use of MazeSuite, and second, to demonstrate the setup and deployment of the fNIR brain activity monitoring system using Cognitive Optical Brain Imaging (COBI) Studio software15. To illustrate our goals, a subsample from a study is reported to show the use of both MazeSuite and COBI Studio in a single experiment. The study involves the assessment of cognitive activity of the PFC during the acquisition and learning of computer maze tasks for blocked and random orders. Two right-handed adults (one male, one female) performed 315 acquisition, 30 retention and 20 transfer trials across four days. Design, implementation, data acquisition and analysis phases of the study were explained with the intention to provide a guideline for future studies.

MazeSuite is a complete toolset to prepare, present and analyze navigational and spatial experiments. Functional near-infrared spectroscopy (fNIR) is an optical brain imaging technique that enables noninvasive and portable monitoring of cerebral blood oxygenation changes. This paper summarizes collective use of MazeSuite and fNIR within a cognitive processing learning paradigm.

MazeSuite is a complete toolset to prepare, present and analyze navigational and spatial experiments1. MazeSuite can be used to design and edit adapted ...

How to Find Effects of Stimulus Processing on Event Related Brain Potentials of Close Others when Hyperscanning Partners

The partners of each pair must be able to pass the McGill Friendship Questionnaire without communicating. Each partner is then seated in front of a screen in one of two adjacent rooms. These rooms are separated by a glass window through which participants communicate to maintain feelings of togetherness while being fitted with the EEG cap. After checking for adequate EEG signals, the glass is covered by a curtain to prevent visual communication. Then, partners must be silent but are instructed to try to feel in the presence of their partner during the entire experiment. Just before it starts, participants are told that each of them will be presented with one image at a time and that these images will occur at the same time for both of them on their own screen. They are also instructed that, for each trial, the simultaneous images will always be different. However, unbeknownst to them, trials are randomized: only half of them are consistent with this instruction and actually include two different images. These trials form the DSC, that is, the different-stimuli condition. The other half of the trials are inconsistent with the instruction. They include two identical images and form the ISC (identical-stimuli condition). After the experiment, participants are sorted into two groups: those who reported that they felt in the presence of their partner during the majority of the trials and those who reported they did not. The impact of the stimulus processing of the partner is found by subtracting the mean voltages of the ERPs of the ISC (inconsistent with the instructions) from the ERPs of the DSC (consistent with the instructions) in at least two time windows (TWs): firstly, in the 75 to 150 ms TW, where the absolute values of these subtractions are greater, especially at right frontal sites, in those who felt in the presence of their partner than in those who did not; secondly, in the LPP time window (i.e., from 650 to 950 ms post onset), where ERPs are significantly less positive in the DSC than in the ISC in those in whom the raw results of the early (75-150ms) subtractions are negative.

This protocol describes key steps involved in assessing the sensitivity of the brain of one person to the stimulus processing of a close other by selecting pairs of partners, recording their electroencephalogram (EEG) simultaneously and computing their event-related brain potentials (ERPs).

The partners of each pair must be able to pass the McGill Friendship Questionnaire without communicating. Each partner is then seated in front of a screen ...

Conducting Hyperscanning Experiments with Functional Near-Infrared Spectroscopy

Concurrent brain recordings of two or more interacting persons, an approach termed hyperscanning, are gaining increasing importance for our understanding of the neurobiological underpinnings of social interactions, and possibly interpersonal relationships. Functional near-infrared spectroscopy (fNIRS) is well suited for conducting hyperscanning experiments because it measures local hemodynamic effects with a high sampling rate and, importantly, it can be applied in natural settings, not requiring strict motion restrictions. In this article, we present a protocol for conducting fNIRS hyperscanning experiments with parent-child dyads and for analyzing brain-to-brain synchrony. Furthermore, we discuss critical issues and future directions, regarding the experimental design, spatial registration of the fNIRS channels, physiological influences and data analysis methods. The described protocol is not specific to parent-child dyads, but can be applied to a variety of different dyadic constellations, such as adult strangers, romantic partners or siblings. To conclude, fNIRS hyperscanning has the potential to yield new insights into the dynamics of the ongoing social interaction, which possibly go beyond what can be studied by examining the activities of individual brains.

The present protocol describes how to conduct fNIRS hyperscanning experiments and analyze brain-to-brain synchrony. Further, we discuss challenges and possible solutions.

Concurrent brain recordings of two or more interacting persons, an approach termed hyperscanning, are gaining increasing importance for our understanding ...

How to Obtain Reliable Visual Event-related Potentials in Newborns

The present study discusses the characteristics of visual event-related potentials (VEPs) and outlines methodological steps for obtaining reliable measurements in newborns. Obtaining high-quality, reliable VEPs is crucial for the early detection of abnormal development of the central nervous system in at-risk newborns, and for implementing successful early interventions. Recommendations are based on a previous study which showed that when post-conceptional age, polysomnography-identified sleep stages, and light-emitting diodes (LEDs) googles as the luminous source are controlled, no more than 4 repetitions of VEP averages are required to obtain replicable recordings, variability decreases, and reliable VEPs can be obtained. By controlling for these sources of variability and using statistical analyses, we were able to clearly and reliably identify the amplitude and latency of three main components (NII, PII and NIII) present in 100% of newborns (n = 20) during active sleep. Recording VEPs during awake states, quiet sleep and transitional sleep is not recommended because VEP morphology may differ significantly from one average to the next, leading to the risk of misleading clinical prognoses. Moreover, it is easier to obtain VEPs during active sleep because this state can be clearly and reliably identified at this stage of development, sleep cycles are short enough to allow measurements to be taken in a reasonable time, and the method does not require new o expensive equipment.

Several important points for obtaining high-quality reliable visual evoked potentials (VEPs) in newborns while minimizing variability and the risk of misleading prognoses are presented.

The present study discusses the characteristics of visual event-related potentials (VEPs) and outlines methodological steps for obtaining reliable ...

Combining Behavior and EEG to Study the Effects of Mindfulness Meditation on Episodic Memory

Although there has been recent interest in how mindfulness meditation can affect episodic memory as well as brain structure and function, no study has examined the behavioral and neural effects of mindfulness meditation on episodic memory. Here we present a protocol that combines mindfulness meditation training, an episodic memory task, and EEG to examine how mindfulness meditation changes behavioral performance and the neural correlates of episodic memory. Subjects in a mindfulness meditation experimental group were compared to a waitlist control group. Subjects in the mindfulness meditation experimental group spent four weeks training and practicing mindfulness meditation. Mindfulness was measured before and after training using the Five Facet Mindfulness Questionnaire (FFMQ). Episodic memory was measured before and after training using a source recognition task. During the retrieval phase of the source recognition task, EEG was recorded. The results showed that mindfulness, source recognition behavioral performance, and EEG theta power in right frontal and left parietal channels increased following mindfulness meditation training. In addition, increases in mindfulness correlated with increases in theta power in right frontal channels. Therefore, results obtained from combining mindfulness meditation training, an episodic memory task, and EEG reveal the behavioral and neural effects of mindfulness meditation on episodic memory.

Here we present a protocol for combining mindfulness meditation training, an episodic memory task, and EEG to understand the behavioral and neural effects of mindfulness meditation on episodic memory.

Although there has been recent interest in how mindfulness meditation can affect episodic memory as well as brain structure and function, no study has ...

Optimizing the Setup and Conditions for Ex Vivo Electroretinogram to Study Retina Function in Small and Large Eyes

Measurements of retinal neuronal light responses are critical to investigating the physiology of the healthy retina, determining pathological changes in retinal diseases, and testing therapeutic interventions. The ex vivo electroretinogram (ERG) allows the quantification of contributions from individual cell types in the isolated retina by addition of specific pharmacological agents and evaluation of tissue-intrinsic changes independently of systemic influences. Retinal light responses can be measured using a specialized ex vivo ERG specimen holder and recording setup, modified from existing patch clamp or microelectrode array equipment. Particularly, the study of ON-bipolar cells, but also of photoreceptors, has been hampered by the slow but progressive decline of light responses in the ex vivo ERG over time. Increased perfusion speed and adjustment of the perfusate temperature improve ex vivo retinal function and maximize response amplitude and stability. The ex vivo ERG uniquely allows the study of individual retinal neuronal cell types. In addition, improvements to maximize response amplitudes and stability allow the investigation of light responses in retina samples from large animals, as well as human donor eyes, making the ex vivo ERG a valuable addition to the repertoire of techniques used to investigate retinal function.

Optimizing the Setup and Conditions for Ex Vivo Electroretinogram to Study Retina Function in Small and Large Eyes

Modification of existing multielectrode array or patch clamp equipment makes the ex vivo electroretinogram more widely accessible. Improved methods to record and maintain ex vivo light responses facilitate the study of photoreceptor and ON-bipolar cell function in the healthy retina, animal models of eye diseases, and human donor retinas.

Measurements of retinal neuronal light responses are critical to investigating the physiology of the healthy retina, determining pathological changes in ...

Inter-Brain Synchrony in Open-Ended Collaborative Learning: An fNIRS-Hyperscanning Study

fNIRS hyperscanning is widely used to detect the neurobiological underpinnings of social interaction. With this technique, researchers qualify the concurrent brain activity of two or more interactive individuals with a novel index called inter-brain synchrony (IBS) (i.e., phase and/or amplitude alignment of the neuronal or hemodynamic signals across time). A protocol for conducting fNIRS hyperscanning experiments on collaborative learning dyads in a naturalistic learning environment is presented here. Further, a pipeline of analyzing IBS of oxygenated hemoglobin (Oxy-Hb) signal is explained. Specifically, the experimental design, the process of NIRS data recording, data analysis methods, and future directions are all discussed. Overall, implementing a standardized fNIRS hyperscanning pipeline is a fundamental part of second-person neuroscience. Also, this is in line with the call for open-science to aid the reproducibility of research.

The protocol for conducting fNIRS hyperscanning experiments on collaborative learning dyads in a naturalistic learning environment is outlined. Further, a pipeline to analyze the Inter-Brain Synchrony (IBS) of oxygenated hemoglobin (Oxy-Hb) signals is presented.

fNIRS hyperscanning is widely used to detect the neurobiological underpinnings of social interaction. With this technique, researchers qualify the ...

Measurement of the Directional Information Flow in fNIRS-Hyperscanning Data using the Partial Wavelet Transform Coherence Method

Social interaction is of vital importance for human beings. While the hyperscanning approach has been extensively used to study interpersonal neural synchronization (INS) during social interactions, functional near-infrared spectroscopy (fNIRS) is one of the most popular techniques for hyperscanning naturalistic social interactions because of its relatively high spatial resolution, sound anatomical localization, and exceptionally high tolerance of motion artifacts. Previous fNIRS-based hyperscanning studies usually calculate a time-lagged INS using wavelet transform coherence (WTC) to describe the direction and temporal pattern of information flow between individuals. However, the results of this method might be confounded by the autocorrelation effect of the fNIRS signal of each individual. For addressing this issue, a method termed partial wavelet transform coherence (pWTC) was introduced, which aimed to remove the autocorrelation effect and maintain the high temporal-spectrum resolution of the fNIRS signal. In this study, a simulation experiment was performed first to show the effectiveness of the pWTC in removing the impact of autocorrelation on INS. Then, step-by-step guidance was offered on the operation of the pWTC based on the fNIRS dataset from a social interaction experiment. Additionally, a comparison between the pWTC method and the traditional WTC method and that between the pWTC method and the Granger causality (GC) method was drawn. The results showed that pWTC could be used to determine the INS difference between different experimental conditions and INS's directional and temporal pattern between individuals during naturalistic social interactions. Moreover, it provides better temporal and frequency resolution than the traditional WTC and better flexibility than the GC method. Thus, pWTC is a strong candidate for inferring the direction and temporal pattern of information flow between individuals during naturalistic social interactions.

This protocol describes partial wavelet transform coherence (pWTC) for calculating the time-lagged pattern of interpersonal neural synchronization (INS) to infer the direction and temporal pattern of information flow during social interaction. The effectiveness of pWTC in removing the confounds of signal autocorrelation on INS was proved by two experiments.

Social interaction is of vital importance for human beings. While the hyperscanning approach has been extensively used to study interpersonal neural ...