ECGaming:

Use Gaming in the context of ECG biofeedback

Projekttitel: An Exploration of ECG Biofeedback and Synchronization in Cooperative Multiplayer Gaming Interfaces

Projektzeitraum:  ab Wintersemester 2023/2024

Overview:

Media devices are slowly evolving to integrate various interfaces, sensors, and feedback mechanisms to enhance user experiences. One such frontier yet to be thoroughly explored is the integration of ECG (electrocardiogram) as biofeedback within multiplayer gaming. ECGaming aims to investigate the potential of such integration, envisioning scenarios where actions, like jumps in a Super Mario-like game, are controlled via the R-R peaks detected in the ECG signal, with the aim of investigating whether this increases heart-synchronization dependent of explicit awareness.

Requirements & Expectations:

  • Currently enrolled Psychology Bachelor's or Master's student seeking to write their thesis with an aim towards a research career.
  • Prior coding knowledge is not a prerequisite, but would be considered beneficial.
  • Willingness to acquire relevant coding skills and utilize open resources, such as YouTube tutorials, GitHub, and platforms like ChatGPT for coding guidance.
  • Strong problem-solving skills with an emphasis on seeking independent solutions before soliciting feedback.
  • A proactive and resourceful approach towards research challenges is essential.

Objectives:

  1. To understand the feasibility and implications of incorporating ECG as biofeedback in multiplayer gaming.
  2. To investigate how heart rate synchronization between players is affected when heartbeats control avatars' actions, either one's own or others', with and without explicit awareness of whose heart is in control.

Scope:

The project will focus on:

  1. Conducting a background literature review on current approaches and methodologies in using heart rate data as biofeedback in gaming and psychology research.
  2. Explore the viability of incorporating heart-feedback in game play (e.g. heart-beat as a  controller for jumping or shooting)
  3. Optional: Develop a simple collaboration-based games using platforms like Pygame (https://www.pygame.org/) that incorporate ECG signals

Tasks & Deliverables:

1. Literature Review:

  • Identify and review research papers, articles, and existing gaming platforms that employ heart rate or other physiological parameters as biofeedback.
  • Present findings and establish a theoretical framework for the project.
  • Prepare a presentation summarizing the project background, its expected outcomes and hypotheses at a FoKo.

2. Game Exploration & Development (Optional):

  • Main research directives are provided by the supervising researchers but the student is also allowed to and encouraged to explore video game applications that can integrate ECG as biofeedback (e.g. classical arcade games or modern video games)
  • Design and develop a simple multiplayer game using Pygame or a similar platform. The game should allow for biofeedback via ECG.

3. Experimental Setup and Data Colection:

  • The experimental design of ECGaming is to be implemented as follows:

In one condition the players control the movements of one avatar, and their jumps are controlled via the same players heartbeats, whereas in the other condition their heart beat controls the jumps of the other player. In separate conditions, participants are either aware of the conditions, or blinded.

  • Supervisors will assist the student in setting up the basic coupling of heart rate and ECG.
  • Student is responsible for participant recruitment via Sona, randomization, and counterbalancing, and data collection. 

4. Data Analysis

The student is expected to carry out the following analyses independently (with feedback upon request):

Preprocessing: Import and clean the raw ECG data to remove any noise or artifacts Using the NeuroKit Python library (https://neuropsychology.github.io/NeuroKit/).

Feature Extraction: Pinpoint R-peaks in the cleaned ECG signals and derive metrics such as instantaneous heart rate and its variability using methods like neurokit2.ecg_peaks() and neurokit2.ecg_rate().

Synchronization Analysis: Measure and visualize the synchronization patterns between players' heart rates, using cross-correlation techniques.

Statistical Analysis: Apply statistical tools to determine the significance of observed synchronization patterns and explore correlations with gameplay scenarios and outcomes.

5. Final Report:

  • Compile all research findings, game development documentation, experimental results, and analysis into a comprehensive report.

Outcomes:

By the end of this project, the student will have gained a profound understanding of the potential and challenges associated with integrating ECG biofeedback into gaming interfaces. They will have obtained hands-on experience in experimental research and in analyzing heart rate data. Should the results prove noteworthy, we will submit the findings to a peer-reviewed journal. Depending on the student's contribution, they will be credited as a co-author.

Wenden Sie sich bei Interesse gerne an jens.pruessner@uni-konstanz.de