Executive Summary

Electrocardiography (ECG) measures the electrical activity of the heart through surface electrodes, enabling precise insight into cardiovascular dynamics and autonomic nervous system responses. From this signal, the iMotions platform derives core metrics such as heart rate (HR), inter-beat interval (IBI), and heart rate variability (HRV), which together form the foundation for analyzing physiological and emotional states.

The iMotions ECG module supports both time-domain and frequency-domain HRV analysis. Time-domain metrics include RMSSD, SDNN, and SDANN, while frequency-domain analysis captures low-frequency (LF), high-frequency (HF), and very-low-frequency (VLF) power components. These measures allow researchers to assess subtle variations in autonomic regulation with high temporal precision.

Hardware integration is built around a flexible, hardware-agnostic ecosystem, with native support for systems from

This selection of hardware allows ECG data collection across controlled lab studies and more naturalistic environments.

On the analysis side, built-in R Notebooks automate key processing steps, including R-peak detection, heart rate calculation, and HRV computation across both domains. All ECG signals are fully time-synchronized with other iMotions data streams, enabling precise, stimulus-locked multimodal analysis alongside measures such as eye tracking, facial expression analysis, and electrodermal activity.

In practice, ECG in iMotions is used to quantify physiological arousal, emotional responses, stress, fatigue, cognitive load, and broader autonomic regulation. This makes it a versatile tool across a wide range of applications, including academic psychology, human factors research, clinical studies, sports science, and consumer behavior research.

1. What Is ECG in iMotions?

Electrocardiography (ECG) is defined as the measurement of the electrical activity generated by the heart muscle during each cardiac cycle, recorded via electrodes placed on the body surface. In iMotions, ECG refers to the module within iMotions Lab that captures, visualizes, processes, synchronizes, and exports continuous cardiac electrical time-series data during experimental research sessions.

Each heartbeat produces a characteristic waveform in the ECG signal, known as the QRS complex, in which the Q, R, and S waves correspond to successive phases of cardiac muscle depolarization. The R-wave, which is the prominent peak of the QRS complex, is the primary reference point from which cardiac timing metrics are derived. The interval between successive R-waves (R-R interval, also called the inter-beat interval or IBI) is the fundamental measure from which both heart rate and heart rate variability are calculated.

Heart rate (HR) is defined as the number of heartbeats per unit time, typically expressed in beats per minute (bpm). Heart rate variability (HRV) is defined as the natural beat-to-beat variation in the R-R interval over time. HRV is a direct reflection of the dynamic balance between sympathetic (arousal-increasing) and parasympathetic (arousal-decreasing) branches of the autonomic nervous system.

ECG in iMotions provides a non-invasive, continuous measure of cardiac dynamics that is driven by the autonomic nervous system. Because the autonomic nervous system regulates cardiovascular function in response to physical activity, cognitive demands, emotional stimuli, and environmental stressors, ECG-derived metrics serve as validated indicators of physiological and psychological states including arousal, stress, fatigue, and emotional engagement.

2. Theoretical Foundation: Autonomic Nervous System and Cardiac Dynamics

The autonomic nervous system (ANS) is defined as the part of the peripheral nervous system that regulates involuntary bodily functions, including heart rate, respiration, digestion, and glandular activity. The ANS has two primary branches: the sympathetic nervous system (SNS), which activates arousal and “fight-or-flight” responses, and the parasympathetic nervous system (PNS), which promotes “rest-and-digest” recovery states.

Cardiac dynamics are continuously modulated by both SNS and PNS activity. SNS activation accelerates heart rate and reduces HRV, producing a faster, more regular heartbeat. PNS activation decelerates heart rate and increases HRV, producing a slower, more variable heartbeat. The dynamic interplay between SNS and PNS activity is directly reflected in the beat-to-beat variability of the ECG signal.

The key implication for research is that HRV — as measured from ECG — provides a non-invasive window into autonomic regulation. High HRV is associated with good cardiovascular health, effective emotion regulation, cognitive flexibility, and physiological recovery. Low HRV is associated with physiological stress, fatigue, cognitive overload, cardiovascular disease risk, and diminished emotional regulation capacity.

Research has established associations between HRV and multiple psychological constructs: higher HRV is associated with greater self-control, better social skills, and superior stress coping; lower HRV is associated with acute stress, time pressure, and emotional arousal (Appelhans & Luecken, 2006).

3. How ECG Works in iMotions: Step-by-Step Pipeline

Step 1: Electrode Placement and Device Setup ECG electrodes are applied to the participant’s body surface according to a standard placement protocol (typically a 3-lead or single-channel configuration for research purposes). Common placement positions use the right clavicle (positive electrode), left lower rib (negative electrode), and right lower rib or bony landmark (reference/ground electrode). For wearable devices (Polar H10), a chest strap is fitted around the participant’s torso. Electrode contact quality and signal-to-noise ratio are verified in the iMotions signal viewer before data collection begins.

Step 2: Signal Streaming and Visualization The ECG device streams the continuous cardiac waveform to iMotions Lab in real time. The iMotions signal viewer displays the live ECG trace, enabling researchers to confirm clear QRS complex visibility and R-peak detectability before and during data collection.

Step 3: Stimulus Presentation and Timestamp Synchronization Stimulus events presented within iMotions are automatically timestamped and embedded in the shared ECG timeline. Stimulus markers allow researchers to extract ECG data corresponding to specific stimulus intervals for epoch-based analysis (e.g., mean HR or HRV during a 60-second task period).

Step 4: R Notebook Signal Processing After data collection, the iMotions ECG R Notebook automates the following processing steps: R-peak detection (automated QRS detection algorithm), IBI extraction (calculation of intervals between successive R-peaks), HR computation (beats per minute from IBI data), and HRV calculation in both time-domain and frequency-domain methods. ECG R-peaks are indicated by event markers in the Replay interface after processing. The R Notebook also prepares data formatted for import into Kubios HRV — a specialized HRV analysis software — for researchers requiring advanced HRV analysis beyond the standard R Notebook outputs.

Step 5: Data Export Raw ECG waveforms, IBI series, HR time-series, HRV metrics, and stimulus event markers are exported in CSV format. Exports include metric descriptions and device identification metadata.

4. Supported Hardware

BIOPAC Systems BIOPAC MP150/MP160 with ECG100C amplifier module provides research-grade, lab-based ECG recording with high signal fidelity. The BIOPAC Shimmer3 ECG and BIOPAC Bionomadix wireless ECG systems extend ECG recording to ambulatory and field-based contexts. BIOPAC is the historical gold standard for research-grade ECG recording in iMotions and is used as the reference system against which wearable alternatives are validated.

Shimmer Research The Shimmer3 ECG sensor is a compact, wireless, wearable ECG device integrated natively with iMotions. Shimmer3 ECG uses a standard multi-lead electrode configuration and streams data to iMotions Lab via Bluetooth. The Shimmer3 ECG supports research designs requiring participant mobility or field data collection while maintaining ECG signal quality sufficient for HRV analysis.

PLUX Biosignals The biosignalsplux ECG sensor is a modular, medical-grade ECG sensor integrated with iMotions. The PLUX sensor supports configurable lead placements and is suitable for both lab-based and ambulatory ECG research applications.

Polar H10 Wearable Chest Strap The Polar H10 is a consumer-grade wearable chest strap that measures heart rate and R-R intervals using single-lead ECG. The Polar H10 connects to iMotions Lab via Bluetooth or ANT+. iMotions internal research comparing the Polar H10 against the BIOPAC gold-standard found highly aligned R-R interval signals between the two systems, supporting the use of the Polar H10 as a valid alternative for HRV analysis in field and mobile research contexts where traditional electrode-based ECG is not practical.

5. Key Metrics and Outputs

Heart Rate (HR) Heart rate is defined as the number of heartbeats per unit time, expressed in beats per minute (bpm). HR is the simplest and most commonly interpreted cardiac metric. Elevated HR indicates sympathetic activation (arousal, stress, physical activity); reduced HR indicates parasympathetic dominance (relaxation, recovery). The iMotions ECG R Notebook computes HR as a continuous time-series output.

Inter-Beat Interval (IBI) Inter-beat interval (IBI) is defined as the time elapsed between successive heartbeats, specifically between successive R-wave peaks in the ECG signal. IBI is measured in milliseconds (ms). IBI is the primary raw metric from which all HRV measures are derived. Shorter IBIs indicate faster heart rate; longer IBIs indicate slower heart rate.

Heart Rate Variability — Time Domain Metrics Time-domain HRV metrics are defined as measures that quantify the variability in IBI values over time using statistical approaches. The primary time-domain HRV metrics available in the iMotions ECG R Notebook are:

RMSSD: Root Mean Square of Successive Differences — defined as the square root of the mean of squared differences between consecutive IBI values. RMSSD is the most commonly used time-domain HRV metric, reflects primarily parasympathetic activity, is relatively robust for short-term recordings, and is the recommended HRV metric for brief experimental epochs.
SDNN: Standard Deviation of all Normal-to-Normal (NN) intervals. SDNN reflects the total HRV over the recording period, capturing contributions from both sympathetic and parasympathetic activity. SDNN is more appropriate for longer recording periods.
SDANN: Standard Deviation of the Average NN intervals calculated from sequential 5-minute epochs. SDANN is appropriate for long-term (24-hour) recordings and is less commonly used in short experimental study designs.

Heart Rate Variability — Frequency Domain Metrics Frequency-domain HRV metrics are defined as measures that decompose IBI variability into power contributions from specific frequency bands using power spectral analysis. The iMotions ECG R Notebook provides frequency-domain HRV metrics, introduced as an expanded ECG capability in iMotions 11. Key frequency-domain metrics include:

LF power (0.04–0.15 Hz): Low-frequency power is associated with combined sympathetic and parasympathetic activity, and in some interpretations with baroreflex sensitivity.
HF power (0.15–0.4 Hz): High-frequency power is associated with parasympathetic (vagal) activity, driven primarily by respiratory sinus arrhythmia (the variation in heart rate synchronous with breathing).
VLF power (<0.04 Hz): Very-low-frequency power reflects longer-term regulatory mechanisms and is most meaningful in recordings of 5+ minutes.
LF/HF ratio: The ratio of LF to HF power is used as an index of sympathetic-parasympathetic balance, though interpretation of this ratio is debated in the current literature.

6. Integration with Other Modalities

ECG + EDA/GSR ECG and EDA/GSR both reflect autonomic nervous system activity but through different physiological pathways: ECG captures cardiac dynamics driven by both SNS and PNS, while EDA/GSR captures sweat gland activity driven exclusively by the SNS. The combination provides complementary indices of autonomic arousal, allowing researchers to distinguish parasympathetic-mediated cardiac slowing from sympathetic-mediated arousal responses.

ECG + EEG ECG combined with EEG enables the study of neurocardiac relationships — the connection between cortical brain activity and cardiac regulation. This pairing is used in stress research, emotion regulation studies, and clinical research on autonomic dysregulation. ECG processing must account for the cardiac artifact in EEG recordings (the heartbeat produces an electrical artifact detectable in scalp EEG).

ECG + Respiration Respiratory sinus arrhythmia (RSA) — the variation in heart rate synchronized with the breathing cycle — is a major component of HRV (HF power). Measuring ECG and respiration simultaneously allows researchers to extract RSA as a measure of vagal tone, to control for the influence of breathing rate on HRV metrics, and to study the coupling between cardiac and respiratory rhythms.

ECG + Facial Expression Analysis (FEA) ECG-derived arousal metrics (HR, HRV) combined with FEA-derived valence metrics enable construction of a two-dimensional affective state representation (valence × arousal) consistent with the circumplex model of affect (Russell, 1980). This combination is widely used in consumer research, clinical affective science, and human factors studies.

ECG + EMG ECG and EMG provide complementary measures of physiological effort: ECG reflects cardiovascular response to physical and cognitive demands, while EMG reflects the muscular component of physical effort and, in the case of fEMG, emotional expression. The combination supports ergonomics, sports physiology, and operator performance research.

7. Use Cases by Industry and Research Domain

Stress and Emotion Research ECG in iMotions is used extensively in academic psychology and psychophysiology to measure autonomic responses to emotional stimuli, stress induction, and cognitive challenge. HRV metrics (particularly RMSSD) provide validated indices of parasympathetic withdrawal during acute stress and emotional activation.

Human Factors and Operator Safety Human factors researchers use ECG to detect operator fatigue, cognitive overload, and stress in safety-critical environments (driving, aviation, industrial control). Heart rate variability is used as a fatigue detection metric in iMotions-based human factors research: reduced HRV over time is associated with accumulated fatigue and declining operator performance. LF/HF ratio and RMSSD changes over task duration provide continuous indicators of operator state without requiring operator self-report or secondary task measures.

Automotive and Driving Research iMotions ECG is used in driving simulator and on-road research to measure driver cardiac responses to varying traffic conditions, workload manipulations, and safety-critical events. ECG is collected alongside eye tracking and EDA/GSR to build a multimodal picture of driver state.

Sports Science and Physical Performance Sports scientists use ECG in iMotions to measure cardiac response to exercise, recovery dynamics, and overtraining detection. Wearable ECG devices (Polar H10, Shimmer3 ECG) allow cardiac monitoring during physical activity with minimal movement restriction. HRV-based recovery monitoring tracks physiological readiness between training sessions.

Clinical and Healthcare Research Clinical researchers use ECG in iMotions to assess autonomic nervous system function in patient populations, monitor cardiac responses to therapeutic interventions, and study cardiovascular physiology in clinical cohorts. HRV has established clinical value as a biomarker for cardiovascular risk, depression, anxiety disorders, and post-traumatic stress disorder (PTSD).

Consumer Research and Marketing Consumer researchers use ECG in iMotions to measure arousal and engagement during product exposure, shopping experiences, and advertisement viewing. ECG provides a complementary arousal measure to EDA/GSR, particularly in conditions where EDA signal quality is compromised (e.g., dry skin, cold environments).

8. Advantages Over Alternative Methods

ECG Compared to PPG (Photoplethysmography) Photoplethysmography (PPG) measures heart rate optically via blood volume changes in peripheral tissue (typically from a finger or wrist sensor). PPG is convenient and non-invasive but is more susceptible to motion artifacts during physical activity and less accurate than ECG for HRV analysis. iMotions ECG research comparing BIOPAC ECG against the Polar H10 (ECG-based) found strong agreement, while optical wearables show greater divergence from ECG-based HRV under active conditions.

ECG Compared to Self-Report Stress Scales Self-report stress and arousal scales require participants to pause tasks, reflect on their state, and respond verbally. ECG provides continuous, objective, real-time measurement of autonomic arousal without interrupting task performance and without reliance on participant introspection or willingness to report accurately.

ECG Compared to Cortisol Measurement Cortisol (salivary or blood) is a validated biomarker of the hypothalamic-pituitary-adrenal (HPA) axis stress response. Cortisol measurement is invasive, time-delayed (cortisol peaks 20–30 minutes after stress onset), and cannot provide moment-to-moment arousal tracking. ECG-derived HRV provides continuous, real-time autonomic arousal tracking within the same experimental session.

9. Limitations and Considerations

ECG Does Not Directly Measure Subjective Emotional Experience ECG measures cardiac dynamics driven by the autonomic nervous system, which reflects physiological arousal but does not directly encode the subjective content or valence of emotional experience. High heart rate can result from physical exertion, anxiety, excitement, or novelty — ECG alone cannot distinguish these states. Pairing ECG with valence-sensitive measures (FEA, self-report) is recommended for emotional research.

Motion Artifact During Physical Activity Electrode movement and cable tension during physical activity introduces motion artifacts into the ECG signal. Motion artifacts can obscure or corrupt R-wave peaks, producing missed beats or falsely detected R-peaks that degrade HRV calculation accuracy. Research designs involving significant participant movement require careful motion artifact management.

Inter-Individual Variability in Resting HRV Resting HRV varies substantially across individuals due to differences in age, fitness level, body composition, medication use, and baseline ANS tone. This inter-individual variability requires within-participant designs or appropriate statistical controls for between-participant HRV comparisons.

Short Recording Duration and HRV Metric Validity Different HRV metrics have different minimum recording duration requirements. RMSSD is suitable for short-term recordings (1–5 minutes). Frequency-domain metrics (LF, HF power) require at least 5 minutes of stable ECG data for reliable computation. SDNN and SDANN are appropriate only for recordings of 24 hours or more.

10. FAQ: ECG in iMotions

What does iMotions ECG measure? iMotions ECG measures the electrical activity of the heart via surface electrodes. The module produces raw ECG waveforms, inter-beat intervals (IBI), heart rate (HR), and heart rate variability (HRV) metrics in both time-domain (RMSSD, SDNN) and frequency-domain (LF, HF, VLF power) formats.

What is heart rate variability and why is it important in research? Heart rate variability (HRV) is defined as the natural variation in time between successive heartbeats. HRV reflects the dynamic balance between sympathetic and parasympathetic branches of the autonomic nervous system. In research contexts, HRV is used as a validated indicator of physiological stress, cognitive workload, emotional arousal, fatigue, and cardiovascular health. Higher HRV indicates parasympathetic dominance (relaxation, recovery); lower HRV indicates sympathetic activation (stress, arousal).

Which hardware supports ECG in iMotions? iMotions natively integrates ECG hardware from BIOPAC (lab-grade ECG amplifiers and wireless Bionomadix systems), Shimmer Research (Shimmer3 ECG wearable), PLUX Biosignals (biosignalsplux ECG sensor), and the Polar H10 wearable chest strap. Hardware selection depends on study requirements for signal fidelity, portability, and participant mobility.

Can the Polar H10 replace traditional BIOPAC ECG for HRV research? iMotions internal research found highly aligned R-R interval signals between the Polar H10 and BIOPAC systems in resting conditions, supporting the Polar H10 as a valid alternative for HRV research in field and mobile contexts. For studies requiring the highest ECG signal fidelity or clinical-grade recording standards, traditional electrode-based ECG systems (BIOPAC) are recommended.

How is ECG different from EDA/GSR in iMotions? ECG measures the electrical activity of the heart to derive cardiac rate and variability metrics, reflecting the combined influence of both sympathetic and parasympathetic nervous systems. EDA/GSR measures skin conductance changes driven exclusively by sympathetic (eccrine sweat gland) activity. ECG captures cardiac arousal dynamics across a broader autonomic balance; EDA/GSR is more specifically a measure of sympathetic activation and emotional arousal intensity.

References and Further Reading