Welcome to EEGMANYANALYSTS
EEGMANYANALYSTS is an innovative project that focuses on the sources of variance in data analysis routines in EEG research.
Unlike previous studies that often explored within-subject effects, EEGMANYANALYSTS focuses on the analysis of inter-individual differences. In addition, we are particularly interested in understanding how choices and characteristics of analysts impact the replicability of findings.
News: Open call for participation!
As a participant in EEGMANYANALYSTS, we invite you to contribute your expertise in EEG analysis. To join, you should have experience working with EEG data and be committed to submitting your analysis scripts, as well as completing short questionnaires within the next six months. We encourage you to analyze the data using your own lab's typical approach, while ensuring the independence of the individual analyst.
In return for your valuable contribution, you will be offered co-authorship in the resulting peer-reviewed articles and hopefully enjoy the satisfaction of helping advance scientific knowledge in the field of EEG research.
Join us today and become an integral part of EEGMANYANALYSTS. Together, we can explore the fascinating realm of inter-individual differences, examine the impact of analyst choices, and strive for increased replicability of our findings.
About the project
We are excited to introduce #EEGMANYANALYSTS, a groundbreaking project aimed at investigating the sources of variance in EEG data analysis routines.
Building upon the success of previous initiatives (#EEGManyPipelines, Silberzahn et al., 2015; Botvinik-Nezer et al., 2020), our project takes a unique approach by focusing on understanding the impact of choices and characteristics of analysts on the replicability of findings.
Key features of #EEGMANYANALYSTS:
Emphasizing Inter-Individual Differences:
We take a unique approach by focusing on between-subject effects, specifically exploring the relation between the Error Related Negatvity (ERN) and measures of individual traits in anxiety. While previous studies have predominantly focused on within-subject effects, our project aims to broaden the perspective with its focus on between-subjects effects. By investigating associations of EEG indices with self-reported traits, we acknowledge the challenges associated with smaller effect sizes and increased error variance compared to within-subject effects.
Our research questions:
1. What are the key inter-individual differences among analysts in their EEG data analysis approaches, and how do these differences impact the outcomes?
2. To what extent do the choices and characteristics of analysts affect the replicability of findings in EEG research?
3. How do literature standards and existing methods hold up in when applied to a typical multi-laboratory dataset, and what are the practical implications for EEG data analysis?
Join us now...
We invite researchers and analysts in the field of EEG to join us in this pioneering project. By participating, you will have access to a carefully collected EEG dataset and be given the opportunity to analyze the data using your typical analysis approach (preferentially fully automated pipelines).
By coming together as a diverse community of analysts, we can shed light on the impact of inter-individual differences and various variables on the replicability of EEG findings. Your valuable contributions will help us identify best practices, refine analysis approaches, and thereby help enhance the quality of EEG research.
...and contribute!
We invite researchers with experience in EEG analysis to contribute to this exciting initiative. By joining our project, you will have the opportunity to make a significant impact on the field of neuroscience and contribute to scientific progress.
Rules for contributing:
To participate, you should have prior experience working with EEG data. By joining, you commit to submitting your analysis scripts and completing all short questionnaires within six months. We encourage participants to analyze the data as typically done in your own lab, maintaining the independence of individual analysts.
Whereas it is preferred that only single analysts who make ALL analysis decisions themselves participate. However, the implementation of the code can be outsourced to an additional contributor who is then also offered authorship.
Your Benefits:
As a participant, you will be offered co-authorship in all peer-reviewed articles resulting from the project, acknowledging your valuable role in advancing scientific knowledge and helping to improve EEG research practices. Your involvement will be instrumental in fostering an open and collaborative scientific community.
Expected timeline:
Subscription and Enrollment Phase (Until Autumn)
During this phase, you will be required to enroll and provide informed consent (e.g. not to share the data or discuss your analysis/details of the study with third parties).
Pre-Data Questionnaire
After enrollment, you will receive a short questionnaire to gather preliminary information about your background and expertise.
Data Analysis Phase
Once enrolled, you will receive the data necessary to prepare your - fully automated - analysis. You are expected to provide your complete data analysis routine, including your code and workspaces. While non-scripted approaches like BrainVision are accepted, preference will be given to analyses conducted in software such as Matlab, R, or Python.
Post-Submission Questionnaire
After submitting your analysis, you will be asked to complete a questionnaire regarding your methodology and decisions made during the analysis process.
Results and Review Questionnaire
In the following year (2024), you will receive the results of our collective analysis. Additionally, you will have the opportunity to provide feedback through a final review questionnaire.
Manuscript Phase
Early 2025 you will be informed about the possible outcomes of this project and be invited to serve as coauthor for the resulting manuscripts.
By joining the EEGManyPipelines project, you will actively contribute to the advancement of EEG research and gain valuable insights into the variability of analysis pipelines. Your participation will contribute to the development of recommendations and best practices in the field.
We appreciate your dedication to scientific rigor and your commitment to promoting transparency and collaboration within the scientific community.
Enroll now and become a part of this exciting project! Together, we can make a difference in the field of neuroscience.
Project leads
Project leads
Dr. Katharina Paul
Universität Hamburg,
Germany.
Prof. Dr. Jan Wacker
Universität Hamburg,
Germany.
"I’m a postdoc at Hamburg University, where I work on relating inter-individual differences in positive affect and motivation with EEG correlates of reward processing and cognitive control. Recently I’ve focused more on the complexity and possible pitfalls of EEG analysis."
"I'm a professor at Hamburg University...
This project is supported by a grant from the German Research Foundation (DFG).
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