Name: Estimate the Cognitive Load Using Electrocardiographic Measure: A Human-AI Collaborative Task (Video) | JoVE Journal
Uploaded: 2025-12-05T14:45:00.000+00:00
Duration: 7 min 8 s
Description: Learn to measure cognitive load variations during human-AI collaborative tasks using electrocardiogram data acquisition. This protocol demonstrates how physiological indicators reveal how task difficulty influences decision-making processes.

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5 minutes

This study was supported by the Postgraduate Research &#38; Practice Innovation Program of Jiangsu Province (KYCX25_4311), National Natural Science Foundation of China (72374088, 72471105, 72472094, 72001096), 2024 Key Project of Higher Education Science Research Planning of China Association of Higher Education (24XX0205, Mechanisms of Human-AI Collaboration Strategies in Enhancing Learning Outcomes), 2024 Key Project of Education Science Planning in Jiangsu Province (B-b/2024/01/162, Model Construction and Empirical Research on Multimodal Learning from the Perspective of Educational Neuroscience) and the Humanities and Social Science Fund of Ministry of Education of China (24YJCZH445).

This study was conducted in accordance with the principles of the Declaration of Helsinki and was approved by the Institutional Review Board of Jiangsu University of Science and Technology. Informed consent was obtained from all participants, who agreed to the use and publication of their data.&#160;No temperature-sensitive or hazardous procedures are included in this protocol.
1. Preparation
<ol>
	<li>Introduce the experimental procedure to participants, and obtain written informed consent after they are fully informed of the study.</li>
	<li>Prepare the ECG data acquisition system and all required materials, including the transmitter, receiver, stretchy Velcro straps, electrode lead set, power cable, M/F Ribbon Cable, ethernet cable, alcohol wipes, and medical tape (see Table of Materials).
	<ol>
		<li>Prepare an adequate number of pre-gelled disposable ECG electrodes.</li>
		<li>Connect the data acquisition system to the ECG data receiver module. Then plug the system into a stable power supply.</li>
		<li>Use the M/F Ribbon Cable to establish a connection between the data acquisition system and&#160;the computer used for stimulus presentation.</li>
		<li>Use an Ethernet cable to connect the data acquisition computer to the data acquisition hardware system.</li>
	</ol>
	</li>
</ol>
2. Instrument setup
<ol>
	<li>Clean the participant&#39;s skin using alcohol wipes at the following sites: below the left rib, below the right clavicle, and on the right abdomen. Attach a disposable ECG electrode patch at each site.</li>
	<li>Connect the lead wires.
	<ol>
		<li>Attach the lead-wire connector securely to the ECG transmitter.</li>
		<li>Connect the metal clip of the red lead wire (positive lead) to the ECG electrode placed below the left rib; connect the metal clip of the white lead wire (negative lead) to the ECG electrode placed below the right clavicle; and connect the metal clip of the black lead wire (ground lead) to the ECG electrode placed on the right abdomen. 
		NOTE: Ensure that each clip is fully seated and does not rotate or loosen after attachment. If a lead wire is longer than needed, segment and secure it to the skin with medical tape to minimize artifacts caused by wire movement.</li>
	</ol>
	</li>
	<li>Secure the ECG transmitter to the participant&#39;s body using stretchy hook-and-loop fastener straps. Adjust the strap tension to keep the transmitter stable while ensuring participant comfort. 
	NOTE: Avoid placing straps directly over the ECG electrode patches to prevent signal distortion or electrode displacement.</li>
</ol>
3. Data acquisition
<ol>
	<li>Turn on the ECG transmitter switch and the power of the ECG data acquisition system, and establish a wireless connection between the ECG transmitter and the ECG receiver of the data acquisition system. 
	NOTE: A correct connection is indicated by green lights on both the ECG transmitter and the ECG receiver.</li>
	<li>Launch the ECG data acquisition software (see Table of Materials), create a new graph file, select the ECG receiver module from the device list, and choose the appropriate ECG recording channel. 
	NOTE: Adjust the channel selection panel on the ECG receiver to ensure that the hardware channel matches the selected software channel.</li>
	<li>Click Start in the data acquisition software interface to begin ECG data collection. Verify that the ECG waveform appears stable on the screen.</li>
	<li>Instruct the participant to begin the experimental task. 
	NOTE: Remind participants to rest their backs comfortably against the chair and maintain a stable posture throughout the tasks.</li>
	<li>After the task is completed, click Stop in the data acquisition software to end the recording session.</li>
	<li>Detach the lead-wire clips from the electrode patches. Loosen the stretchy hook-and-loop fastener straps and remove the ECG transmitter from the participant&#39;s body.</li>
	<li>Instruct and assist the participant in gently removing the disposable electrode patches by hand.</li>
</ol>
4. Data processing
<ol>
	<li>Open the recorded ECG data file in the data analysis software (see Table of Materials).</li>
	<li>Select the ECG channel and apply digital filtering.
	<ol>
		<li>In the top menu bar, navigate to Transform &#62; Digital Filters &#62; Comb Band Stop.</li>
		<li>In the dialog box, select Fixed at under Base Frequency. Enter 50 Hz and click OK to apply the filter. 
		NOTE: The frequency entered should correspond to the power line frequency of the country where the data are collected.</li>
		<li>Click&#160;Transform&#160;in the top menu. Select&#160;Digital Filters &#62; FIR &#62; Band Pass. Under&#160;Frequency cutoff, select&#160;Fixed at&#160;and enter 1 Hz. Under&#160;High Frequency cutoff, choose&#160;Fixed at&#160;and enter 35 Hz.</li>
	</ol>
	</li>
	<li>Create Focus Areas according to the experimental design.</li>
</ol>
5. Data analysis
<ol>
	<li>Generate and export HR data. From the menu bar, select Analysis &#62; Hemodynamics &#62; ECG Interval Extraction. In the Analyze panel, choose Focus Areas Only, and in the Display results as panel, select Excel Spreadsheet Only. Click OK to confirm. 
	NOTE: R-peak detection was performed using the built-in QRS detection algorithm in data analysis software (AcqKnowledge 5).</li>
	<li>Generate HRV data and export both time-domain and frequency-domain results.
	<ol>
		<li>Open Analysis, select HRV and RSA &#62; Multi-epoch HRV - Statistical. In the ECG Channel field, select the preprocessed ECG channel. Under Extract HRV statistics for, select Focus Areas. In Output results to, select Spreadsheet. Finally, click OK to generate and export the HRV time-domain results.</li>
		<li>Open Analysis again, select HRV and RSA &#62; Multi-epoch HRV and RSA - Spectral. In the ECG Channel field, select the preprocessed ECG channel. Under Extract HRV and RSA for, select Focus Areas. In Output type, select Excel Spreadsheet Only. Finally, click OK to generate and export the HRV frequency-domain results.</li>
	</ol>
	</li>
</ol>

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</ol>

The authors have nothing to disclose.

In this study, the quality of ECG signal acquisition was critical for subsequent analyses. During the experimental procedure, correct electrode placement and proper connection with the leads were essential prerequisites for accurate signal recording. All electrodes were placed at the designated positions and ensured good skin contact to minimize impedance. In addition, to reduce the influence of external factors on physiological signals, participants were instructed to abstain from caffeine before the experiment and to a...

Cognitive load refers to the total amount of mental activity imposed on an individual's cognitive system within a given period of task performance1. Elevated cognitive load can impair human performance and is a major contributor to workplace accidents2. High cognitive load states affect cardiac activity by activating the sympathetic nervous system and inhibiting the parasympathetic nervous system, thereby increasing heart rate (HR) and decreasing heart rate variability (HRV)3. Expectancy beliefs refer to the extent to which individuals believe they can successfully complete a specific task or activity4. Task difficulty influences individuals' expectancy beliefs, which in turn regulate their behavioral engagement and allocation of cognitive resources, ultimately manifesting as different levels of cognitive load.
Electrocardiography (ECG) provides a high-temporal-resolution record of the heart's electrical activity. HR and HRV indices derived from ECG signals capture different aspects of cardiac rhythm and serve as psychophysiological indicators of an individual's physical and mental stress5. HR reflects the overall frequency of cardiac contractions and can be used to assess the basic state of the cardiovascular system. HRV represents the temporal variation between successive heartbeats and serves as an indicator of the autonomic nervous system (ANS) regulation, reflecting the dynamic balance between sympathetic and parasympathetic activity6,7,8. The sympathetic nervous system is activated under stress, threat, or high-intensity activity, often leading to increased HR and decreased HRV. Conversely, the parasympathetic nervous system is activated during relaxation, sleep, or low-energy-demand states, typically resulting in decreased HR and increased HRV.
Current physiological methods for measuring cognitive load include ECG activity, electroencephalographic (EEG) activity, respiration, blood pressure, and ocular indices9. Compared with traditional retrospective questionnaires, physiological measurements provide a better means of capturing participants' real-time states, thereby avoiding recall bias and the temporal delay effects inherent in self-report approaches. Portable ECG devices enable continuous and unobtrusive assessment of participants' state while they are engaged in ongoing tasks10. Compared with other physiological measurement techniques, portable ECG represents a low-cost, non-invasive method that allows participants to perform tasks in a more natural and flexible manner within their environment11. Average HR tends to increase as cognitive task difficulty rises12, and HRV has been shown to be highly responsive to both mental and physical demands13. Considering that the present task is expected to impose substantial mental effort and a modest level of physical load, and given the practical needs of task delegation scenarios in human-AI collaboration, HR and HRV are appropriate indices for measuring cognitive load.
HR and HRV have been widely applied to the study of cognitive load across multiple domains, including aviation14, driving15, and economic decision-making16, providing powerful tools for the continuous and objective assessment of human states. With the growing integration of AI into the workplace, understanding changes in cognitive load during human-AI collaboration has become increasingly important. One study investigating AI-assisted decision-making found that under conditions of high cognitive load, humans exhibited an irrational reliance on AI-generated suggestions17. In collaborative settings, the role of AI may extend beyond providing advisory input, enabling humans and AI to jointly complete tasks. The present study focuses on a novel collaboration mode in which humans have task-allocation authority and may choose, for each task, whether to complete it themselves or delegate it to the AI. However, in this specific context where humans have task-allocation authority, it remains unclear how task difficulty influences decision-making behavior and, consequently, reflects different levels of cognitive load. Therefore, this study aims to employ portable ECG devices to measure HR and HRV with precision, thereby objectively quantifying the dynamic changes in cognitive load under this collaborative paradigm.
The human-AI collaborative task selected for this experiment is a general task adapted from the paradigm proposed by Fügener et al.18. The advantage of using such a generalizable task is that it does not require participants to undergo specialized training and can be readily transferred to more domain-specific tasks. In the study described here, the experimental task involves image classification, in which participants are asked to assign a target image to one of several image category groups. A classification is considered correct if the selected category group matches the true category of the target image. The target images were drawn from the ImageNet database, where categories have already been validated. For each category group option, ten example images were provided to participants to reduce the likelihood that they might be unaware of certain categories19. We employed GoogLeNet Inception v3 as the AI model20, which had been trained on 1,000 potential image categories and achieved an average classification accuracy of 0.76.
We conducted a preliminary experiment to determine the difficulty level of the target images. In this preliminary experiment, participants were required to classify all images on their own. The sample size of 217 participants in the present study was determined with reference to the human-only condition reported by Fügener et al.18. In a collaborative task, it is generally desirable to assign each task to the party best suited to perform it. To operationalize task difficulty, we compared the accuracy for each image with the AI's average accuracy. Images with human accuracy lower than the AI's average accuracy were defined as those more suitable for AI completion -- that is, images that are relatively difficult for humans. In the formal experiment, we selected 32 images in total, including 16 easy images and 16 difficult images.
An a priori power analysis was conducted using G*Power 3.1 for a Wilcoxon signed-rank test21. We assumed a medium effect size, a two-tailed α level of 0.05, and a desired power of 0.90. The analysis indicated that a minimum of 47 participants was required. We recruited 60 students from the campus who had not previously participated in similar experimental tasks. The inclusion criteria were as follows: all participants were right-handed, had not taken any medication recently, had abstained from caffeine and alcohol for 24 h prior to the experiment, had not engaged in vigorous physical exercise on the day before the experiment, had maintained adequate sleep the night before the session, and reported no cardiovascular or respiratory diseases. The participants' mean age was 22.77 ± 1.25 years. The experimental setup is shown in Figure 1.
Before the experiment began, participants were informed that they would perform an image-classification task together with an AI. They were told the AI's average accuracy and instructed that, for each target image, they could either classify it themselves or delegate the classification to the AI. If participants chose to complete the task themselves, they responded by pressing one of the five keys (A, B, C, D, or E) on the keyboard to select an answer from the five image options. Pressing the designated key (P) indicated that the task was delegated to the AI, although participants could not see the AI's chosen answer. A marker was written at both the onset and the offset of each stimulus for subsequent analysis. Before the formal experiment began, participants completed 4 practice trials, followed by a 120 s resting period. They then proceeded to the 32-trial formal experiment. A 10 s inter trial interval was provided between each trial. The experimental procedure is illustrated in Figure 2.
<img alt="ECG setup diagram: lead placement and data acquisition for cardiac signal analysis." class="xfigimg" src="/files/ftp_upload/69621/69621fig1v2.jpg" data-altupdatedat="1784001604000" data-alt="Figure 1"> 
Figure 1: Experimental setup. The figure shows the equipment used in the experiment and the placement of ECG measurement electrodes. (A) Schematic of ECG electrode placement; (B) Experimental setup. <a href="https://www.jove.com/files/ftp_upload/69621/69621fig1v2large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="ECG experiment flowchart showing process steps: wear device, task, end; monitoring heart activity." class="xfigimg" src="/files/ftp_upload/69621/69621fig2.jpg" data-altupdatedat="1784001604000" data-alt="Figure 2"> 
Figure 2: Experimental procedure. The practice session consisted of 4 trials, and the formal experiment included 32 trials. The experimental program was developed using E-Prime (see Table of Materials). Abbreviation: ECG = Electrocardiogram. <a href="https://www.jove.com/files/ftp_upload/69621/69621fig2large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>

<table><tbody><tr><td>Alcohol wipes</td><td>-</td><td>-</td><td>This is used to clean and disinfect the skin.</td></tr><tr><td>Acqknowledge 5.0</td><td>BIOPAC</td><td></td><td>This is software for signal data acquisition and data analysis.</td></tr><tr><td>Desktop computer</td><td>Dell</td><td>OptiPlex</td><td>This desktop is used to run the software for ECG acquisition and analysis.</td></tr><tr><td>ECG data acquisition device</td><td>BIOPAC</td><td>MP160</td><td>This device is used to record ECG data.</td></tr><tr><td>ECG electrodes</td><td>Shenfeng</td><td>S series</td><td>This is an accessory used to collect ECG signals.</td></tr><tr><td>Electrode lead set</td><td>BIOPAC</td><td>BN-EL45-LEAD3</td><td>This is used for data logging along with the transmitter.</td></tr><tr><td>Ethernet cable</td><td>BIOPAC</td><td>-</td><td>This is used to connect the MP160 system and the data acquisition and analysis software.</td></tr><tr><td>Experimental software</td><td>PSYCHOLOGY SOFTWARE TOOLS</td><td>E-prime3.0 Build 3.0.3.31</td><td>This is the software used to make the experimental program.</td></tr><tr><td>Laptop</td><td>Dell</td><td>E5470</td><td>This laptop is used to run the experimental program and present the stimuli.</td></tr><tr><td>M/F Ribbon Cable</td><td>BIOPAC</td><td>CBL110C</td><td>This is used to transmit specific signals.</td></tr><tr><td>Medical tape</td><td>-</td><td>-</td><td>This is used to secure long wires.</td></tr><tr><td>Power cable</td><td>BIOPAC</td><td>-</td><td>This is used to power the device.</td></tr><tr><td>Receiver</td><td>BIOPAC</td><td>BN-RSPEC-R</td><td>This is a ECG receiver.</td></tr><tr><td>Stretchy Velcro straps</td><td>BIOPAC</td><td>BN-STRAP-137&amp;nbsp;</td><td>This is used to help fix the ECG transmitter.</td></tr><tr><td>Transmitter</td><td>BIOPAC</td><td>BN-RSPEC-T</td><td>This is a wireless ECG transmitter.</td></tr></tbody></table>

estimate the cognitive load using electrocardiographic measure: a human-ai collaborative task

Estimate the Cognitive Load Using Electrocardiographic Measure: A Human-AI Collaborative Task

In workplace settings, individuals' cognitive load is closely tied to both performance and safety. As collaboration between humans and artificial intelligence (AI) becomes a growing trend, the ability to capture cognitive load dynamics in a real-time and objective manner during collaborative processes has become increasingly critical. Traditionally, cognitive load has been assessed through questionnaires; however, such retrospective methods are limited in their capacity to accurately capture the moment-to-moment fluctuations in cognitive load throughout task execution. In contrast, physiological signals allow for continuous monitoring of an individual's real-time state, thereby providing a more objective measure of cognitive load. Task difficulty may influence participants' expectations, which subsequently shape their behaviors and ultimately affect cognitive load. This study implements a generalizable human-AI collaborative task that incorporates multiple levels of difficulty. The average accuracy of the AI system was disclosed to participants prior to the start of the task. In each trial, participants were given the option either to complete the task themselves or to delegate it to the AI. The aim of this protocol is to use electrocardiographic (ECG) data as physiological indicators to examine how individuals' cognitive load changes when they have the authority to allocate tasks and collaborate with AI under different difficulty levels. This study further explores the internal state dynamics revealed by physiological data during human-AI collaboration. The findings provide a scientific basis for improving collaboration models, enhancing efficiency, and leveraging the complementary strengths of humans and AI to achieve better resource integration in complex tasks.

In the physiological measurements, we focused on HR and HRV indices in both the time and frequency domains. For the time domain, we selected Root Mean Square of Successive Differences (RMSSD), which is considered a robust measure22,23,24. For the frequency domain, although the Low Frequency/High Frequency ratio (LF/HF) is often interpreted as reflecting the balance between sympathetic and parasympathetic activity, this interpret...

This protocol describes the use of physiological indicators to measure variations in cognitive load across different task difficulty levels in human-AI collaborative tasks. The findings suggest that people adapt their decision-making processes according to task difficulty, thereby reflecting different levels of cognitive load.

In workplace settings, individuals' cognitive load is closely tied to both performance and safety. As collaboration between humans and artificial ...

We study how people's cognitive load changes when working with AI in a delegation setting. To begin, introduce the experimental procedure to the participant, and ensure they're fully informed about the study. Prepare the electrocardiogram data acquisition system and gather all necessary materials, including the pre-gelled, disposable electrocardiogram electrodes.Connect the data acquisition system to the electrocardiogram data receiver module. Using the male to female ribbon cable, establish a connection between the data acquisition system and the computer used for stimulus presentation. Using an ethernet cable, establish a connection between the data acquisition system and the computer used for data recording.Plug the system into a stable power supply. Now, clean the participant's skin with alcohol wipes at three sites. Below the left rib, below the right clavicle, and on the right abdomen.Attach one disposable electrocardiogram electrode patch to each clean site. Then attach the lead wire connector securely to the electrocardiogram transmitter, and connect the metal clip of the lead wire to the corresponding electrode. Secure the electrocardiogram transmitter to the participant's body using stretchy hook-and-loop fastener straps and adjust the strap tension to ensure the transmitter stays in place and remains comfortable for the participant.Turn on the switch of the electrocardiogram transmitter and power on the electrocardiogram data acquisition system. Check the wireless connection status between the transmitter and the receiver module of the data acquisition system. Now, launch the electrocardiogram data acquisition software.Create a new graph file. Select the electrocardiogram receiver module from the device list. Choose the appropriate electrocardiogram recording channel and click okay in the newly displayed transmitter connection status window.Click start in the data acquisition software interface to begin electrocardiogram data collection. Verify that the electrocardiogram waveform appears stable on the screen. Now, instruct the participant to begin the experimental task.After the task is completed, click stop in the data acquisition software to end the recording session. Detach the lead wire clips from the electrode patches. Loosen the stretchy hook-and-loop fastener straps and remove the electrocardiogram transmitter from the participant's body.Instruct and assist the participant in gently removing the disposable electrode patches by hand. Next, open the recorded electrocardiogram data file in the data analysis software. Select the electrocardiogram channel and apply digital filtering.In the menu bar, navigate to transform followed by digital filters and comb band stop. Then in the dialog box, select fixed at under base frequency. Enter 50 hertz and click okay to apply the filter.Next, click transform in the menu bar. Select digital filters, then FIR, followed by band pass. In the dialogue box, under low-frequency cutoff, select fixed at and enter one hertz.Under high-frequency cutoff, select fixed at and enter 35 hertz. Now, create focus areas based on the experimental design, Generate and export heart rate data by selecting analysis, followed by hemodynamics and ECG interval extraction from the menu bar. In the analyze option, choose focus areas only and in the display results as option, select Excel spreadsheet only.Click okay to confirm and complete the export. To generate heart rate variability data, open analysis and select HRV and RSA followed by multi-epic HRV statistical. In the ECG channel field, select the pre-processed electrocardiogram channel and under extract HRV statistics for choose focus areas.Go to output results to, select spreadsheet, and click okay to generate and export the time domain results. Finally, open analysis again and select HRV and RSA followed by multi-epic HRV and RSA spectral. In the ECG channel field, select the pre-processed electrocardiogram channel.Under extract HRV and RSA for, choose focus areas. In output type, select Excel spreadsheet only, and click okay to export the frequency domain results. Mean heart rate was significantly lower under the difficult image condition compared to the easy image condition.High-frequency heart rate variability was significantly higher during the difficult image condition compared to the easy image condition. Root mean square of successive differences, followed a similar trend and was significantly higher during the difficult image condition. Participants delegated significantly more frequently under the difficult image condition than the easy image condition.We found that people adjust their cognitive load by delegating tasks when working with AI.In future, we will look at how different co-learning approaches affect outcomes when human and AI learn together.

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Research

JoVE Journal

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Learn to measure cognitive load variations during human-AI collaborative tasks using electrocardiogram data acquisition. This protocol demonstrates how physiological indicators reveal how task difficulty influences decision-making processes.

Estimate the Cognitive Load Using Electrocardiographic Measure: A Human-AI Collaborative Task

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