ElectroTools

Electroencephalography (EEG) is a powerful non-invasive neuroimaging technique used to measure electrical activity of the brain directly from the scalp. Since its discovery almost a century ago, EEG has been widely used to investigate brain dynamics in health and disease. Yet, despite its long history, popularity and success, questions have been raised recently about the robustness of historical EEG findings and highlighted the limitations of current practices. This is because high-profile results are seldom replicated, experiments typically employ small samples and there is a large variation across laboratories in their approach to analysing the same phenomena.

Through the #EEGManyLabs network, we have mobilised a global collection of researchers to replicate 20 of the most influential studies ever published. Our project will capitalise on this unprecedented degree of enthusiasm and engagement from across the research community, leverage the data generated by this network and bootstrap it through the development of a suite of modular openly accessible tools and resources that will transform approaches to experimental design, data collection and analysis.

In this project, we will: (i) curate the world's largest open-access EEG data library; (ii) develop tools and resources that facilitate and promote multi-site collaboration, which will support the generation of larger and diverse samples; (iii) generate realistic synthetic datasets that can be used to benchmark and document the impact of analytical choices on outcomes; (iv) create automated analysis pipelines suitable for the most widely employed EEG research designs; (v) define lower-bound estimates of effect sizes for the most commonly reported EEG phenomena; (vi) provide a web-tool to help researchers make well-informed decisions about trial numbers and sample sizes; and (vii) deliver an end-of-project workshop and collection of onboarding video tutorials & documentation.

Collectively, these tools and resources will help facilitate a step-change in the robustness of, and confidence in, future EEG research. Through a commitment to open science practices and the development of easy-to-use and accessible tools, we will lower the barrier to entry for new researchers who want to take advantage of the opportunities afforded by EEG and help experienced researchers transition towards open science practices. This work will also support clinicians and the rapidly growing neurotechnology industry who wish to make use of EEG signals to deliver products and services that improve brain health and deliver societal and economic impact.

Electroencephalography (EEG) is the oldest and most widely used non-invasive neuroimaging technique available. Yet, despite its popularity, there are major question marks about the replicability of EEG findings and the rigour and robustness of common research practices. Small sample sizes, large variations in methodology and little standardisation in analysis pipelines are commonplace. This limits the interpretation and utility of research outcomes.

Through #EEGManyLabs, we have mobilised an international network of researchers to replicate the most influential EEG studies ever published. We will use this platform to collect an unprecedented amount of data and develop a suite of tools that support each stage of the scientific discovery process.

By documenting laboratory variation in EEG signals and developing transformation techniques for data curation and batch correction, we will create tools for the harmonisation of data recorded from different sites, allowing for larger and more diverse samples.

We will generate synthetic datasets with different degrees and types of noise to allow benchmarking of pre-processing and statistical analysis methods. By comparing all justifiable analytical decisions on these data, we will provide optimised, automated analysis scripts.

To help design efficient and effective experiments, we will create an effect size library for the most commonly studied phenomena that can be used to support interpretation and design future experiments.

Finally, we will provide a web-based graphical user interface, detailed documentation, video tutorials and a free virtual workshop on how to use these resources to allow new and experienced researchers to onboard and engage in open science practices.

By achieving these objectives, we will facilitate a cultural shift towards high-powered multi-site collaboration, and provide a robust foundation for the translation of EEG to medical and commercial applications.