Abstract National Science Centre in Poland supported preparing this article with a research grant (UMO-2020/39/B/HS6/00685) and a scholarship awarded to M.B. by the Foundation for Polish Science (FNP). The funder had no role in study design, data collection, analysis, publishing decisions, or manuscript preparation. The project was conducted in AMU Psychophysiology Lab: Positive Gaming & […]
Abstract
National Science Centre in Poland supported preparing this article with a research grant (UMO-2020/39/B/HS6/00685) and a scholarship awarded to M.B. by the Foundation for Polish Science (FNP). The funder had no role in study design, data collection, analysis, publishing decisions, or manuscript preparation. The project was conducted in AMU Psychophysiology Lab: Positive Gaming & Streaming (head: Lukasz Kaczmarek). The authors would like to thank Lukasz Kaczmarek for his help in funding acquisition and consultations on the early versions of the project.
Background & Summary
Participants detailed their highest level of competitive play (ranging from recreational to international), their professional engagement with esports (as a full-time or part-time job or a non-income generating activity), and their weekly gaming duration in hours. Additionally, they reported their in-game ranking (specifically, the top rank achieved in the past year), the total time spent playing the CS: as recorded by the CS: GO game system within the Steam Library (Valve Corp., USA), average weekly hours spent playing against computer-controlled opponents (bots). We also calculated their total gaming hours over the tournament preceding two weeks from their daily logs. Participants also shared their age and Body Mass Index (BMI).Article PubMed
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Ethics Information
The physiological and behavioral data, in a CSV format, constitute 366 GB of space32. The files are grouped into eight subcomponents with a maximum size of 50 GB due to OSF storage restrictions. The component’s name indicates for which study stage (S1 vs S3) and participants and which person they refer to. For instance, the name ‘Physio_S3_225_300’ indicates that the component included psychophysiological and behavioral data from Stage 3 for participants from 225 to 300. The component contains a set of CSV files for particular subjects. All psychophysiological and behavioral signals recorded during the experiment for each individual are available in a single CSV datafile named “S < stage_id > _P < participant_id >,” where “S” stands for study stage, “P” for participants, e.g., S1_P10.csv, or S3_P224.csv. The “< particpant_id >” is a natural number identifying a participant.
The CEPAV dataset is available at OSF1. This dataset holds potential value for a broad range of fields within affective science, including psychology, for exploring the connections between self-reported individual differences and physiological responses; computer science, particularly in the areas of machine learning for the development of automated affect detection and the clustering of gaming-related data; physics, as a practical dataset for examining the technicalities of signal processing; and mathematics, for the verification of mathematical or statistical models. The dataset can also be used to explore methods for performance optimization and examine factors related to gamers’ well-being and ill-being. To simplify the use of the CEPAV dataset, we also recorded two videos in which we discuss 1) the motivation, procedures, and results of the initial study6, and 2) the structure of the CEPAV dataset. Both videos are available on the OSF1. As the dataset is published under CC-By Attribution 4.0 International license, we hope that CEPAV will provide individuals, companies, and laboratories with the data they need to perform their analyses to advance affective science.
Internal/external validity: our study offered a unique blend of internal and external validity through the use of controlled experiments paired with real-world outcomes. It included a thorough evaluation of affective and physiological dynamics and implemented a robust, theory-driven intervention.
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Fig. 2
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Data preprocessing
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Maciej Behnke, Wadim Krzyżaniak, Patrycja Chwiłkowska, Szymon Jęśko Białek, Maciej Kłoskowski, Patryk Maciejewski & Kacper Szymański
Data Records
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Dataset Structure
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Declaration of Generative AI and AI-assisted technologies in the writing process
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Code availability
Gameplay during the CS: GO matches was captured using the Open Broadcaster Software25 at 30 FPS to document in-game actions and strategies (Fig. 2).
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Other psychophysiological datasets related to affective manipulations exist (see Tables 1–3), but these datasets are usually related to one of the data types included in CEPAV. We reviewed existing openly available datasets of physiological responses to affective manipulations. Compared to the CEPAV strengths, we found that only four databases included physiological, behavioral, and video data along with individual differences measures8,9,10,11. Seven databases included data collected on multiple occasions10,12,13,14,15,16,17. Only one database included more participants than the CEPAV18 dataset. Finally, we found five datasets that included gaming, including simple labyrinth games9, racing games19,20 shooter games platform games19, FIFA21, and League of Legends16. Only one dataset included data on participants’ performance16, while other datasets used gaming as a situational context for the study.
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The upper-body recordings in an MP4 format, full HD resolution (1920 × 1080) constitute 820 GB of space31. The files are grouped into 19 components with a maximum size of 50 GB due to OSF storage restrictions. The component’s name indicates for which study stage (S1 vs S3) and participants and which person they refer to. For instance, the name ‘Video_S3_255_280’ indicates that the component included a video record from Stage 3 for participants from 255 to 280. To ease the video data analysis, we included the time intervals for each experimental epoch in the “CEPAV_data/video_timing” sheet24 (e.g., for Stage 1 for Participant 8, the baseline starts at 1:17 minutes of video).Article CAS PubMed PubMed Central
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In our initial publication, we tested the effects of the Synergistic Mindsets Intervention (SMI) compared to a control intervention6. The SMI was positively received, leading participants to adopt more advantageous stress mindsets, more favourable appraisals of the esports tournament, and an increased application of reappraisal strategies for emotion regulation. Despite these positive outcomes, the high-stakes nature of the esports competition was perceived as an enjoyable challenge rather than a negative stressor, reducing the potential for the SMI to significantly influence affective and physiological reactions. The absence of a negative physiological stress response meant there was very little for the intervention to modulate. Consequently, no significant changes were noted in affective responses or gaming performance due to the intervention. Access to the research code, dataset, and findings can be found elsewhere6.
We converted the raw acquired data (obtained with proprietary acquisition software) into a consistent format and saved it in CSV files. All signals were resampled to 1 kHz, using the previous neighbor interpolation method. Signals from different devices were time-synchronized using synchronization markers generated by VU-AMS and Finometer devices during experiments. We marked the baselines, matches, and recoveries within the files. Finally, data across studies were exported to normalized form, consisting of a header, predefined file structure, and standardized subject naming convention. The description of labels used for tagging specific epochs is available in the “CEPAV_data” file24, the “epoch_name” sheet.
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Esports refers to competitive video gaming where individuals compete against each other in organized tournaments for prize money. Here, we present the Competitive Esports Physiological, Affective, and Video (CEPAV) dataset, in which 300 male Counter Strike: Global Offensive gamers participated in a study aimed at optimizing affect during esports tournament1. The CEPAV dataset includes (1) physiological data, capturing the player’s cardiovascular responses from before, during, and after over 3000 CS: GO matches; (2) self-reported affective data, detailing the affective states experienced before gameplay; and (3) video data, providing a visual record of 552 in-laboratory gaming sessions. We also collected (affect-related) individual differences measures (e.g., well-being, ill-being) across six weeks in three waves. The self-reported affective data also includes gamers’ natural language descriptions of gaming affective situations. The CEPAV dataset provides a comprehensive resource for researchers and analysts seeking to understand the complex interplay of physiological, affective, and behavioral factors in esports and other performance contexts.
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Visualization Single Physiological File Structure for Stage 1 (left panel) and 3 (right panel). ECG- electrocardiogram, mV; Z0 – Average thorax impedance, ohm; dZ – Change in impedance due to respiration and heartbeat, ohm; dZ/dt – Impedance CardioGram, ohm/s; SBP – Systolic Pressure, mmHg; DBP – Diastolic Pressure, mmHg; MAP- Mean Pressure, mmHg; HR – Heart rate, bpm; SV – Stroke Volume, ml; LVET – Left Ventricular Ejection Time, ms; PI- Pulse Interval, ms; MS – Maximum Slope; mmHg/s; CO – Cardiac Output; l/min; TPR – Total Peripheral Resistance Medical Unit, mmHg.min/l; TPRCGS – Total Peripheral Resistance CGS; dyn.s/cm5; wr – right wrist movement, custom units; tl – left thigh movement, custom units; tr – right thigh movement, custom units.
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Shifts in Mean Levels of Affective Measures. Red line separates Stage 1 and Stage 3 laboratory visits. HR – Heart rate, bpm, SBP – Systolic Pressure, mmHg; DBP – Diastolic Pressure mmHg; CO – Cardiac Output; l/min; TPR – Total Peripheral Resistance Medical Unit, mmHg.min/l.
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We collected the number of kills, kills’ assists, and deaths and the match scores as determined by the Counter-Strike: Global Offensive scoring system, which factors in the difficulty of the weapon used and the points earned for each enemy bot eliminated. A higher score reflects superior performance. Gamers’ tournament performance was primarily evaluated based on their total score, making it the main performance index. However, other metrics—kills, assists, and deaths—can provide insight into the participant’s strategy. For instance, a high total score paired with a high number of deaths may indicate a risk-taking approach. Analyzing which strategies proved optimal or suited individual gamers could be valuable for esports coaches, helping them tailor training and game plans effectively. In Stage 2, participants were asked to log their daily match scores, simulating the conditions of the upcoming tournament.Download references
However, the dataset comes with certain limitations. First, this dataset cannot be employed to investigate differences between sexes, ethnicities, or between the group ages, as all participants were male Caucasian young adults. Second, our investigation was confined to affective reactions within a single esports (Counter Strike: Global Offensive) context. Third, as noted in the missing data section, the dataset lacks some data due to technical constraints (e.g., ICG missing due to electrode detachment), lack of consent to share data, and human errors (e.g., not starting data collection for accelerometers). Lastly, the dataset represents a secondary use of data initially collected for a previously published independent study.Accepted:
We present questionnaires, open-text, other self-reported data, and auxiliary information about the participants in the “CEPAV_data” spreadsheet24. The file includes participants’ ID, sex, age, height, weight, experimental conditions, and questionnaire responses (the “self_reports” sheet). To make it easier to use the database, we also included averages for physiological and behavioral data from selected moments of the study in the file, which were used for the Summary of the Physiological, Affective, and Behavioral Activity During the Competitive Esports (Technical Validation section) and presented in Fig. 5 (the “physio_behav” sheet)24.Uusberg, A. et al. Appraisal shifts during reappraisal. Emotion23, 1985–2001 (2023).
Participant movement was non-invasively tracked using three tri-axial accelerometers (model wGT3X-BT, Actigraph, USA), placed on the thighs (thigh left and thigh right; TL & TR) next to the knee and the right wrist (WR), allowing for the continuous observation of physical activity and gestures during gameplay. All accelerometers were initialized before the participant’s arrival to collect raw acceleration data at 30 Hz with the same start time using ActiLife software (version 6.13.4). We used the measure of the vector magnitude for the given accelerometer for each 1-second interval extracted with the ActiLife software.Behnke, M., Krzyżaniak, W., Nowak, J. et al. The competitive esports physiological, affective, and video dataset. Sci Data12, 56 (2025). https://doi.org/10.1038/s41597-024-04364-z
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Participants’ upper bodies were continuously captured on video using an HD camera positioned in between the monitors, utilizing the Open Broadcaster Software25. The camera was set approximately 65 cm away from the participants’ heads, with a recording at 30 FPS. We also captured the back view of the experimental view with the camera near the ceiling (Fig. 2). These recordings were primarily used to monitor the study’s progress and ensure participant safety.Medland, H., De France, K., Hollenstein, T., Mussoff, D. & Koval, P. Regulating emotion systems in everyday life: Reliability and validity of the RESS-EMA scale. Eur. J. Psychol. Assess.36, 437 (2020).
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Affective science is an interdisciplinary field that draws upon methods and findings from psychology, cognitive science, neuroscience, computer science, biology, and other related fields to understand the complexities of affective phenomena and to determine how they influence human behavior. Multimodal data are needed to thoroughly investigate affective phenomena. Different scientific backgrounds equip researchers with unique skills for data collection. For example, psychologists excel in experimental designs, computer scientists excel in data mining from digital platforms, and biomedical researchers excel in collecting biological samples. Interdisciplinary teams leverage these diverse methodologies to approach research questions by collecting comprehensive multi-modal datasets.
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The physiological data quality was assured by following recommendations in affective science33. First, the data were collected by experimenters who completed at least 30 hours of training in psychophysiological research provided by MB. Second, prior to performing preprocessing, the first author (MB) visually inspected all physiological signals. Before inclusion in the database, MB manually double-checked all datasets for missing or corrupted data.
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We collected data on individual differences at the beginning of Stage 1 and Stage 3 and during the 1-month follow-up. Details related to each questionnaire are presented in Tables 4–6. Additional details on the included questionnaires (e.g., which items measure specific subscales) are included in the “CEPAV_data” spreadsheet24 (the “self_reports” sheet).
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We collected six types of measures in this project, namely questionnaires, open-text, other self-reports, video, physiology, and behavior.
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Multistage: participants provided data on multiple occasions, namely during two laboratory sessions, daily gaming, daily descriptions of the day, and one-month follow-up after the esports tournaments.
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To describe competitive gaming from the affective, physiological, and behavioral perspectives, we calculated the averages for the one-minute intervals for given situations within the study: five minutes of resting baseline, pre-match baselines, during the matches, and recoveries after the matches. Figure 5 visualizes the levels of the given parameters: HR, HRV, SBP, DBP, CO, TPR, WR, TL, and TR. As shown, there are clearly visible differences between gaming and resting periods for HR, HRV, and accelerometry. Furthermore, we identified the time-related drift in HR, which may indicate the healthy habituation process39,40. Our observations may serve as an inspiration for the quantitative analysis of the observed phenomena.
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Virtanen, P. et al. SciPy 1.0: Fundamental algorithms for scientific computing in Python. Nat. Methods17, 261–272 (2020).Upon their first visit to the lab, participants were briefed on the study’s procedures and gave informed consent, specifying which data they were willing to share publicly (for details on how many participants were unwilling for their data to be shared publicly, see Technical Validation/Missing Data section). Next, psychophysiological sensors were attached, and participants completed baseline questionnaires (Tables 4–6). Instructions and questionnaire responses were managed through two 23-inch PC monitors (one dedicated to gaming and the other to experimental tasks). Following the setup and questionnaire completion, the researcher checked the signals quality and moved from the experimental room to the control room, signaling the start of the experiment. The researcher monitored the safety and participant behavior using the camera installed behind the participant.
We also prepared examples in Python Jupyter Notebooks presenting how to load and visualize psychophysiological data from sample files (Fig. 3). Both the conversion scripts and the Notebooks can be obtained from our source code repository available at CEPAV Code Component30 and at GitHub https://github.com/psychosensing/CEPAV.
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During the period between laboratory visits, participants were instructed to play CS: GO as per their usual gaming routine. On gaming days, they were asked to designate one match to be played in “performance mode”, mirroring tournament conditions. This involved playing a match that included a 2-minute preparation phase and providing affective self-reports prior to the game (see Fig. 1). Post-match, they were asked to log their scores. Additionally, at the day’s end, participants were asked to report their affect and the total time spent gaming that day.We organized a large-scale study around esports competition6. We conducted a three-phase experiment involving a novel Synergistic Mindsets Intervention, compared to a control group focused on brain education7, to explore its impact on training and competition performance. This included monitoring participants’ progress over two weeks and their performance in a tournament. A one-month follow-up assessed the intervention’s long-term effects. We recorded gamers’ functioning in the laboratory sessions with continuous, non-invasive measurements of physiological, behavioral, and video data (Stages 1 & 3). Strengths of the CEPAV dataset include:
Download citationEach of the CSV files in the dataset has a 1-line header. The header includes channel/sensor name e.g., “timestamp”, “ECG”, “SBP”, or “marker”. Following the header, each CSV file contains 20 columns. The first column of the data table (except for the header) contains timestamps, as provided by a clock on the main data acquisition (logging) computer – the timestamp shows the time of data collection. In the last column, there is a marker that identifies the specific phase of the experiment. The “epoch_name” sheet provides a full explanation of the epoch IDs used to mark the specific phase of the experiment, e.g., “1” indicates the experimental baseline, while “13” indicates the first minute of the baseline match during Stage 1. The columns in between timestamp and marker contain the physiological data and behavioral data, namely ECG (column 2), DZ (3), DZDT (4), Z0 (5), SBP (6), DBP (7), MAP (8), HR (9), SV (10), LVET (11), PI (12), MS (13), CO (14), TPR (15), TPRCGS (16), WR (17), TL (18), TR (19). For the visualization of a single physiological file structure for Stages 1 and 3, see Fig. 3.
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Following these matches, they were asked to evaluate the negative appraisals and undertake an emotion recall task. In an emotion recall task, participants were instructed to reflect on, detail, and assess two distinct moments from the tournament: one that elicited positive emotions and another that elicited negative emotions. Participants evaluated these situations on the dimensions of positive and negative affective experience, appraisals, and affect regulation strategies.Kang, S. et al. K-EmoPhone: A mobile and wearable dataset with in-situ emotion, stress, and attention labels. Sci. Data10, 351 (2023).
As shown in Tables 4–6, the questionnaires used present acceptable reliability.
M.B., D.L., K.P., P.C, J.P.J & J.J.G designed research. M.B., & J.J.G secured funding. M.B., D.L., K.P., J.P.J & J.J.G wrote the initial manuscript. All authors reviewed and edited the final version of the manuscript. M.B. and P.C. coordinated the experimental research and managed the data processing, analysis, and OSF repository of the project. M.B. & D.L. contributed to the analysis plan. M.B., P.C., S.J.M., M.K., W.K., P.M., & K.S. performed the experiments, collected data, and pre-processed the data. M.B., run the analysis. W.K. coded the software for the data preprocessing. M.B., J.N., S.K., W. K., developed the dataset, M.B., W. K., and J.N. contributed to the technical validation. S.K. supervised the data processing and technical validation.Article PubMed
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Cardiovascular biosignals were captured using the Finometer MIDI device (Finapres Medical Systems, Netherlands). This device employs the volume-clamp technique with finger cuffs placed on the third digit of the left hand to measure arterial pressure waveforms in the finger, enabling the estimation of Systolic Pressure (mmHg; SBP); Diastolic Pressure (mmHg; DBP); Mean Pressure (mmHg; MAP); Heart rate (bpm; HR); Stroke Volume (ml; SV); Left Ventricular Ejection Time (ms; LVET); Pulse Interval (ms; PI); Maximum Slope (mmHg/s; MS); Cardiac Output (l/min; CO); Total Peripheral Resistance Medical Unit (mmHg.min/l; TPR); Total Peripheral Resistance CGS (dyn.s/cm5; TPRCGS);Two weeks following the initial laboratory session, participants returned to compete in the esports tournament. This session mirrored the setup of Stage 1, starting with physiological sensor attachment, completing questionnaires, and undergoing a 5-minute physiological baseline. Subsequently, participants played eight tournament matches.
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In the first week of this phase, the SMI participants were prompted to use reappraisal before their match to regulate their affective responses. The following week, this prompt was not given to assess if participants would autonomously utilize reappraisal techniques in their daily gaming. The SMI participants also documented their adherence and progress within the affect regulation training by reporting gaming scenarios where they employed reappraisal (also serving as an intervention booster). Control participants noted their gaming situations without any specific affective characteristics. We used the Smartphone Ecological Momentary Assessment 3 (SEMA 3) application for Stage 2 data collection23.Behnke, M. et al. CEPAV Dataset, Code Component. Open Science Frameworkhttps://doi.org/10.17605/OSF.IO/GFZ3M (2024).
Jan Nowak & Szymon KupińskiSample of the Video Data Collected During Stage 1 and Stage 3. The depicted participant provided explicit consent for the open publication of his image.
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We present data collected in the Psychophysiological Laboratory at the Faculty of Psychology and Cognitive Science, Adam Mickiewicz University, by the International team of researchers for the study aimed at optimizing affective responses during esports tournament6 from April 2023 to September 2024 in Poznan, Poland. Additional information on research questions, methods, and analysis are available elsewhere6.Matar Boumosleh, J. & Jaalouk, D. Depression, anxiety, and smartphone addiction in university students—A cross-sectional study. PLoS One12, e0182239 (2017).
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The sample consisted of 300 male players of Counter-Strike: Global Offensive (CS: GO), aged between 18 and 32 years, with a mean age of 21.95 years (SD = 2.29). Competitive experience varied within the group: 200 players (67%) had no experience, 76 (25%) had competed in local tournaments, 17 (6%) had participated nationally, and six (2%) had taken part in international competitions. One participant did not disclose his competitive background. Esports provided an additional income source for 17 participants, while the rest did not earn money through gaming. On average, participants had been playing CS: GO for 9.13 years (SD = 5.22), with a mean total gameplay time of 2225.69 hours (SD = 1980.55) as recorded in their Steam Library (Valve Corp., SA). The number of participants varied across CS: GO ranks, with 9 participants ranked as Silver I, 2 as Silver III, 7 as Silver IV, 4 as Silver Elite, 4 as Silver Elite Master, 10 as Gold Nova I, 14 as Gold Nova II, 12 as Gold Nova III, 11 as Gold Nova Master, 28 as Master Guardian I, 21 as Master Guardian II, 26 as Master Guardian Elite, 27 as Distinguished Master Guardian, 29 as Legendary Eagle, 32 as Legendary Eagle Master, 19 as Supreme Master First Class, 44 as Global Elite. Details related to inclusion and exclusion criteria and the process of sample size determination are described elsewhere6.
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Project and Match Procedures. The red frames represent a procedure for all performances (to simplify the figure, we depicted it in detail only for baseline performance), namely prematch physiology, affective experience, Counter-Strike: Global Offensive match, and recovery. Baseline and post-intervention questionnaires include negative prior mindsets, positive and negative affective experiences, affect regulation strategies, well-being, ill-being, alexithymia, and emotion belief measures. Affective self-report includes affective experience and demands and resources evaluation. Emotion recall tasks include recalling and describing situations from the tournament that elicited positive and negative affective experiences and evaluating them using affective experience, situational appraisals and affect regulation strategies measures. One month after Stage 3, participants were asked to fill in follow-up questionnaires, the same set as at baseline and post-intervention. Figure reproduced from our previous article6, used under a CC BY license.
Participants were asked to provide open-text answers while learning about the SMI and control intervention, in daily reports, and during the Emotion Recall Task. The full script of Polish and English materials is available elsewhere6. The exact instructions for open-text questions are presented in Tables 7–9.Article
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The session concluded with a study evaluation, after which participants were debriefed, screened for any suspicions regarding the study’s aims, and compensated. Finally, we identified esports tournament winners, namley players that collected the most points in eight matches (separately, in the SMI and control group). The top three players in each group were awarded prizes of 2500, 1500, and 1000 PLN for first, second, and third places, respectively, equivalent to approximately 600, 360, and 240 USD. We also awarded a single player in each group that scored the most points in a single match, with the award of 500 PLN.Behnke, M., Buchwald, M., Bykowski, A., Kupiński, S. & Kaczmarek, L. D. Psychophysiology of positive and negative emotions dataset of 1157 cases and 8 biosignals. Sci. Data9, 10 (2022).
Multimodality: participants provided 750 hours of cardiovascular, behavioral, and video data during the laboratory sessions. Furthermore, participants answered multiple questionnaires related to gaming-related affect, well-being and ill-being, and answered multiple open-text questions related to affective gaming situations.Monsoon, A. D., Preece, D. A. & Becerra, R. Control and acceptance beliefs about emotions: Associations with psychological distress and the mediating role of emotion regulation flexibility. Aust. Psychol.57, 236–248 (2022).
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The calculated SNR indicated the high quality of all collected signals35, with mean SNR ranging from 21.90 dB to 68.05 dB depending on the physiological signal and study. We identified outliers with the median absolute deviation, with a cutoff of 3, as recommended by Leys et al.36,37, resulting in 416 signals (3.94% of all calculated SNR values) identified as SNR outliers. Next, the first author (MB) visually inspected all flagged data to determine whether the signal should be deleted and classified as an artifact, resulting in 17 signals being identified as artifacts. Outlying signals are marked with yellow color in the “SNR” sheet with additional comments from the first author on reasons that potentially influenced the outlying values (e.g., disconnecting ECG and ICG electrodes after the study while the signals were still being recorded).
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Miranda-Correa, J. A., Abadi, M. K., Sebe, N. & Patras, I. Amigos: A dataset for affect personality and mood research on individuals and groups. IEEE Trans. Affective Comput.12, 479–493 (2018).The study received approval from the Bioethical Committee at Poznan University of Medical Sciences in Poland (802/22). Written informed consent was obtained from all participants, specifying which types of data—self-reports, physiological, behavioral, and video—they agreed to share publicly. This process ensured participants explicitly consented to data publication and the sharing of their images while also allowing them the option to opt out of sharing any specific type of data. For their participation, individuals were compensated with a shopping voucher worth 400 PLN (approximately 0). In addition, esports tournament winners were awarded prizes of 2500, 1500, and 1000 PLN for first, second, and third places, respectively, equivalent to approximately 600, 360, and 240 USD.
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The initial phase of the experiment involved a 5-minute period for establishing physiological baselines, requiring participants to sit quietly. This was followed by the first CS: GO match to assess baseline performance in a format that would mirror the subsequent tournament without any experimental manipulations (baseline match). Post-match, participants were randomly divided into either the SMI or a control group, where they engaged in a 30–40-minute self-guided intervention. Participants were randomly assigned using a random number generator from RandomLists. The assigned condition was linked to the participant number and hardcoded into the intervention presentation software. Upon reviewing the intervention materials, participants were prompted to integrate the newly acquired strategies into their esports training, which involved playing two additional matches (training matches 1 & 2). To conclude Stage 1, instructions were provided on how to log daily measures. Synergistic mindsets participants were advised to consistently apply affect regulation techniques during their gaming sessions over the next two weeks. Control participants were encouraged to incorporate the learned brain facts into their daily gaming practices.Article PubMed MATH
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Participants played Counter-Strike: Global Offensive (CS: GO) in a deathmatch mode on the Dust II map, competing against the highest difficulty level of computer-generated opponents (bots) without random weapons. All matches were structured into prematch baseline measurements (2 minutes), gaming (2 minutes), and recovery (2 minutes) (Fig. 1). During all match phases, we collected cardiovascular, behavioral, and video data. Before each match, participants also shared their affective experience and provided demands and resources evaluations.
William, W. K. Zung self-rating depression scale. Encycl. Qual. Life Well Being Res 7317 (2014).The “CEPAV_data” spreadsheet details missing data for various types, organized under the columns: Missing_ICG_ECG, Missing_Blood_Pressure, Missing_Accelerometry, and Missing_Video (the “missing” sheet)24. Specifically, we were unable to collect some ICG/ECG data from 10 participants, blood pressure data from 10 participants, accelerometry data from 9 participants, and video data from 5 participants. Furthermore, for 7 participants, we deleted some signals that were identified as artifacts during SNR analysis. The spreadsheet also specifies the study stage at which the data is missing; for example, S3_m15 indicates missing data during Stage 3, encompassing tournament performance 5 recovery and tournament performance 6 baseline. Additionally, the absence of ICG and ECG data impacted our ability to compile complete data files for 11 sessions for 10 participants. Furthermore, 22 participants withheld consent to share their video data. None of the participants withheld consent to share their self-reports and behavioral and physiological data.
Huang, W., Liu, G. & Wen, W. MAPD: A Multi-subject Affective Physiological Database. In Computational Intelligence and Design (2014).The code can be accessed via the Code component30 on OSF and via the public GitHub repository: https://github.com/psychosensing/CEPAV. It is licensed under MIT OpenSource license, i.e., the permission is granted, free of charge, to obtain a copy of this software and associated files (e.g., the Jupyter IPython Notebooks), subject to the following conditions: the copyright notice and the MIT license permission notice shall be included in all copies or substantial portions of the software based on the scripts we published. Scripts for converting data from proprietary acquisition software formats into consistent CSV files, as well as IPython Jupyter Notebooks presenting how to load the data from CEPAV CSV files into Python Pandas DataFrame structure, are available in the Code component30 on OSF and at the following GitHub repository: https://github.com/psychosensing/CEPAV. Scripts that we used to transform the data from proprietary acquisition formats into coherent CSV files utilized Python 3.10.1241. The list of the specific modules and their versions is available in the “requirements.txt” file in the GitHub repository. Jupyter Notebooks use Python version: 3.10.12, as well as the following Python modules: packages related to Jupyter Notebook: jupyterlab v. 4.0.8; jupyter-core module v. 5.5.0, jupyter-client v. 8.6.0; jupyter_server: 2.10.0, Nbclient v. 0.9.0, nbconvert v. 7.11.0, nbformat v. 5.9.2, traitlets v. 5.13.0ipython v. 8.17.2; ipykernel v. 6.26.042; and a data organization and manipulation module – pandas v. 2.2.228.
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Acknowledgements
Faculty of Psychology and Cognitive Science, Adam Mickiewicz University, Poznan, Poland
Participants attended two individual laboratory sessions (Fig. 1). During the first session, baseline data and training-related measures were collected (Stage 1). Participants were then randomly assigned to one of two groups: a control group, which learned general facts about the brain, or the Synergistic Mindsets Intervention (SMI) group, which focused on using reappraisal techniques to approach both the performance situation and their responses to it more productively. Further information on the SMI, control interventions, and cover story and transcripts of all instructions given to participants (in Polish and their English translations) were published as the supplementary materials of the hypothesis testing article and can be found elsewhere22. Those in the SMI group practiced reappraisal strategies over a two-week period, documenting their adherence and improvements (Stage 2). The follow-up laboratory visit saw participants competing in an esports tournament (Stage 3). We then explored the SMI’s enduring effects with a 1-month follow-up.John, O. P., Donahue, E. M. & Kentle, R. L. The Big Five Inventory – Versions 4a and 54. (University of California, Berkeley, Institute of Personality and Social Research, 1991).
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The Vrije Universiteit Ambulatory Monitoring System (VU-AMS, Netherlands) was employed to collect cardiac biosignals, incorporating both impedance cardiography (ICG) and electrocardiography (ECG) for continuous and noninvasive monitoring of heart activity. In accordance with psychophysiological standards26,27, we utilized pre-gelled AgCl electrodes (Kendall Abro, H98SG) positioned according to the standard Lead II setup for ECG measurements and arranged in a four-spot pattern for ICG assessments. This device enables the continuous recording of ECG, Average thorax impedance (ohm; Z0); change in impedance due to respiration and heartbeat (ohm; dZ), and Impedance CardioGram (ohm/s; dZ/dt) to check for proper quality of the recorded signals. Raw data exports were carried out using the VU-AMS Data, Analysis & Management Software (VU-DAMS 5.4.20).Article PubMed MATH
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We also calculated the mean values of each channel for Stage 1 and Stage 3. Histograms (Fig. 4) present that collected signals had standard ranges. For instance, most participants presented a healthy SBP and DBP range during the experiments38. Figure 4 presents data for easily interpretable measures (e.g., HR, SBP, DBP collected with Finometer) and does not present raw recordings (e.g., ECG in mV) that require further processing (e.g., heart rate based on peak analysis).
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In conclusion, the CEPAV database offers a rich and detailed source of data shared with the scientific community to support research and development in the field of affective science. One of the principles of open science is that researchers are free to use the dataset without the need to contact the authors as long as their usage complies with the applicable licenses. However, we encourage researchers to reach out to us at macbehnke@gmail.com if they have any questions or require assistance. We are happy to provide guidance and support to help maximize the utility of the dataset. Article PubMed MATH
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Here, we present the Competitive Esports Physiological, Affective, and Video (CEPAV) dataset1. Esports represents a rapidly growing field in which well-trained individuals – gamers – compete using video games. In esports, gamers compete while seated in front of a screen, creating an ideal environment to study affective responses, including emotional experiences and real-time cardiovascular reactions to performance2,3,4,5. This setting allows for the examination of high-stakes performance with continuous real-time monitoring of affective responses at multiple levels. Using esports as a model allowed us to gain insights into the interplay between emotional states and physiological responses during intense gameplay sessions.
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