Global Open Science Electrophysiology

Pedro Valdes-Sosa Organizer
University of Electronic Science and Technology
School of Life Sciences
Chengdu, Sichuan 
Maria Bringas-Vega Co Organizer
University of Electronic Science and Technology of China
Chengdu, Sichuan 
Alan Evans Co Organizer
McGill Centre for Integrative Neuroscience (MCIN)
Montreal, Quebec 
Qing Wang Co Organizer
Shanghai Mental Health Center
Shanghai, [Select a State] 
Sunday, Jun 23: 9:00 AM - 5:30 PM
Educational Course - Full Day (8 hours) 
Room: Grand Ballroom 104 
The Global Brain Consortium's educational program addresses a topic of critical importance rooted in contemporary challenges faced in neuroscientific research. The insights from the 223rd OHBM meeting highlight the urgent need for the inclusive development of multimodal imaging practices across diverse geographic regions. In response, the Global Brain Consortium has actively developed innovative tools to overcome barriers and foster global collaboration in narrow imaging. The Local Organizing Symposium further emphasizes the consortium's commitment to addressing these challenges.

The imperative now is to educate and train a substantial number of students and young researchers. The consortium's tools are not only open source but also leverage available data, aligning with the evolving landscape of neuroscientific research. This initiative strategically responds to the need for collective efforts to empower the next generation of researchers with skills to navigate advanced tools effectively.

The educational program's programming is as follows:

1. Overview of the GBC strategy for electrophysiology. Alan Charles Evans. Distinguished James McGill Professor of Neurology and Psychiatry at McGill University.
2. The importance of standardized (HED) event description in creating analysis-ready datasets for open science functional neuroimaging. Scott Makeig. Research Scientist & Director, Swartz Center for Computational Neuroscience (SCCN) Institute for Neural Computation (INC) University of California San Diego.
3. The CONP infrastructure for this work. Alan Charles Evans. Distinguished James McGill Professor of Neurology and Psychiatry at McGill University.
4. The CBRAIN and LORIS neuroinformatics ecosystem. Bryan Lawrence Caron. Director, Operations and Development, NeuroHub - Montreal Neurological Institute - Hospital, McGill University.
5. EEGNet and the tagging tools and collaborations built through GBC. Christine Rogers. Research Project Officer (CONP, EEGNet) at McGill Centre for Integrative Neuroscience, Montreal Neurological Institute, McGill University.
6. The Chinese WeBrain interface to GBC. Yun Qin. Full Proffesor at the University of Electronic Sciences and Technology of China.
7. Introduction of qEEG. Pedro A. Valdes-Sosa. MD, Ph.D., Dsc., Director of the Joint China-Cuba Collaboratory for Translational Neuroscience. Full Professor at the University of Electronic Science and Technology of China.
8. qEEG toolbox. Jorge Bosch-Bayard. Associate Researcher at the Autonomous University of Madrid.
9. Formulating clinical applications using qEEG: examples. Maria Luisa Bringas. Professor at the University of Electronic Science and Technology of China.
10. Causality and mediation tools for q-EEG and clinical applications. Qing Wang (Vincent). Assistant Researcher at Shangai University.
11. Concluding remarks. Pedro A. Valdes-Sosa. MD, Ph.D., Dsc., Director of the Joint China-Cuba Collaboratory for Translational Neuroscience. Full Professor at the University of Electronic Science and Technology of China.

The program, led by Alan Charles Evans, Distinguished James McGill Professor of Neurology and Psychiatry at McGill University, and Pedro A. Valdes-Sosa, MD, Ph.D., Dsc., Director of the Joint China-Cuba Collaboratory for Translational Neuroscience (, aims to equip participants with a comprehensive understanding of the Global Brain Consortium's open data ecosystem. This knowledge, coupled with practical skills gained, will enable participants to contribute meaningfully to the inclusive and collaborative future of electrophysiology research. The timeliness of this educational program lies in its proactive response to the global call for inclusivity and the urgency to train the next wave of researchers in cutting-edge tools and methodologies.


Participants in the educational program will achieve three specific learning objectives aligned with ACCME purposes. Firstly, they will gain a thorough understanding of the Global Brain Consortium's (GBC) open data ecosystem for multimodal processing. This involves familiarizing participants with the ecosystems developed by the GBC, providing insight into the tools and workflows crucial for neuroinformatics.
Secondly, participants will acquire practical knowledge through hands-on experiences with key neuroinformatics tools, including Neymar and C Brain, as presented by Alan and Scott McCabe. This objective emphasizes active engagement with online tools within the general neuroinformatics ecosystem, ensuring participants develop practical proficiency for their research endeavors.
Thirdly, the program aims to equip participants with the skills to translate data into clinical applications. By learning how to transition from data sharing to addressing clinical questions, participants will be empowered to apply their knowledge in real-world scenarios, contributing to advancements in clinical applications within the field of electrophysiology. These three objectives collectively address the ACCME's emphasis on practical application, skill development, and the dissemination of knowledge in the evolving landscape of neuroscientific research.

Target Audience

The target audience for the educational program primarily comprises junior researchers, young PhD students, and postdoctoral scholars who are either entering the field of electrophysiology or are in the process of preparing to become principal investigators (PIs). The focus is on individuals in the early stages of their careers, seeking to enhance their knowledge and skills in neuroscientific research within the framework of the Global Brain Consortium.
If planning to attend, bring your laptops and have access to CBRAIN/LORIS.  


1. A sea change in functional neuroimaging towards computation-rich open science: building and mining analysis-ready data

The dominant paradigm in academic research has long been for new results to be produced by individual investigator laboratories operating on data they collect themselves. The typical cycle is for small experiments to be proposed, recorded, analyzed (typically, using a single measure), and published without regard for further use of the collected data. This cycle has two inefficiencies. One is statistical: marginal results obtained from small studies may be difficult to reproduce. Another is economic: discarding carefully collected data before its information content has been fully mined is inherently wasteful.
Currently, therefore, more research funding is being directed toward large studies whose information can be mined by many computationally oriented investigators. When (and only when) the data are made available in a form that is truly analysis-ready, this can be highly productive. However, current statistical methods including machine learning demonstrate the power of richly varied data for learning to emulate, predict, and diagnose activity in complex systems such as the brain. Thus, there is value in collecting and jointly mining smaller, both existing and new datasets collected using differing paradigms under differing conditions – again when those datasets have been made available in analysis-ready form.
Making human functional neuroimaging data analysis-ready has a major difficulty. Human experience, cognition and behavior are highly complex and time varying, thus the brain dynamics supporting them must be as well. This makes the problem of recording and describing what happened during human neuroimaging experiments both essential and challenging. Current practice followed by both experimentalists and imaging equipment manufacturers is to record only a quite sparse representation of what the participant(s) experienced during data recording – e.g., onset times of events whose types are recorded using ad hoc codes such as ‘Event-type 127.’ These codes typically differ from experiment to experiment – highly constricting full analysis of their event-related brain dynamics, both within and across datasets. The problem of annotating events using a common vocabulary and syntax is more acute for electromagnetic brain data, as its fine temporal grain enables study of dynamics supporting individual thoughts, actions, and reactions.
Remarkably, only one system for annotating events in time series data has been proposed and developed: the system of Hierarchical Event Descriptors (HED). ‘HED’ (or ‘H-E-D’) annotation is accepted in all the BIDS modality formatting standards, but is still little known or adopted. Developed as an open source community project on github (see, HED and its growing tool infrastructure is now ready for widespread use, broader community participation, and further extension. I will describe its structure, will illustrate its use, and will offer exercises for course students to familiarize them with using HED.


Scott Makeig, University of California San Diego San Diego, CA 
United States

2. The CONP ecosystem infrastructure for neuroinformatics.

This section will describe the organizational and Informatics infrastructure that supports the Global Brain Consortium (GBC). Specifically, the GBC uses the LORIS multi-modal database and the CBRAIN high-performance computing platform to curate, analyze, and disseminate data. These platforms constitute major infrastructure elements supporting national networks like the Canadian Open Neuroscience Platform (CONP) and EEGNet. The EEG-centric GBC international network builds upon this mature IT base and its associated organizational framework (administration, ethics, data governance policy, training) to engage with the global community for data- and tool-sharing and project management. Participants will come away with a strategic perspective of the entire ecosystem. In subsequent hands-on sessions, details of how to interact with this ecosystem will be provided. 


Alan Evans, McGill Centre for Integrative Neuroscience (MCIN) Montreal, Quebec 

3. The use of CBRAIN and LORIS neuroinformatics environment

CBRAIN ( is an open source, web-based, collaborative research software platform that addresses major challenges in big data research. CBRAIN allows scientists to launch, monitor and share large-scale big data analyses using advanced scientific tools through an easy to use web-based interface. LORIS ( is a web-based data and project management software for collecting, storing and processing neuroimaging, clinical, biospecimen and genetic data. It is used to manage databases for large scale, multi-site and multi-modal neuroimaging studies. As the two principal components of the NeuroHub ecosystem of neuroinformatics tools, CBRAIN and LORIS work together to provide researchers with a powerful integrated toolbox of software services for research.

A hands-on interactive demonstration of the use of CBRAIN and LORIS will be given. Course participants will learn how to work with MEG/EEG datasets using CBRAIN and LORIS. Through a series of hands-on exercises, participants will carry out the selection of MEG/EEG datasets using the LORIS Data Query Tool within EEGnet and subsequently process the selected datasets using CBRAIN. Participants will learn how to easily explore and work with the processing results, as well as how to download and share those results with collaborators. 


Bryan Caron, Dr., McGill University Montréal, Quebec 

4. Global extensions of the open EEGNet platform and EEG-BIDS standard development

The EEGNet open data and analytics platform brings together standardized datasets, workflows, tools and computational infrastructure for collaborative annotation, visualization, querying and analysis of multi-modal EEG-BIDS datasets across high-performance computing clusters. EEGNet encompasses lossless ICA pipelines and quantitative EEG toolboxes in addition to facilitating data harmonization with open-source cross-platform utilities designed to reduce barriers to data sharing and transparency in federation of datasets. As an end goal, the growing data collections on the EEGNet platform provide a collaborative basis for applications in machine learning, early identification of biomarkers and translation to applications in global health. In partnership with the Global Brain Consortium (GBC), EEGNet also leverages groundwork by the Canadian Open Neuroscience Platform (CONP) for cross-collaborative models of extensible ethics and data governance in open science.  


Christine Rogers, McGill University Montreal, quebec 

5. Use EEG pipelines on WeBrain platform

Scalp electroencephalography (EEG) is a commonly used and excellent technique that directly quantifies the electric fields of brain activity with millisecond temporal resolution, and until recently, EEG processing steps were common, with small incremental steps toward more standardized methods and steps. With the current evolution of ‘cloud neuroscience’ has led to increased research on large-scale EEG applications, resulting in large amounts of EEG data, tools and pipelines on the cloud. Yet most researchers, especially the beginners, are not aware of this and do not make full use of these modern tools and resources. This educational session aims to address this gap, bringing best practices of WeBrain platform ( in EEG processing and sharing to all EEG researchers. The learning outcomes are (1) understanding of the variety of EEG artefacts during recording and conducting quality assessment of continuous raw EEG data; (2) understanding of the standardized processing methods: filtering, artefact removal procedures, REST re-referencing etc.; (3) best practices in EEG processing steps from raw data to EEG measures (power, network, nonlinear, microstate, qEEG etc.) on the platform; (4) best practices in using the power of cloud high-performance computing (combing with CBRAIN platform) or off-line pipelines (WeBrain EEGPipe software) on your large-scale EEG data. 


Yun Qin, University of Electronic Science and Technology of China Chengdu, Sichuan 

6. Principles of quantitative EEG analysis (qEEG)

The participants will grasp the intuition behind frequency domain analysis of the EEG. They will be able to distinguish to which EEG recording it is applicable and tose that are not. They will grasp the major ways of analyzing activation and connectivity in the frequency domain. They will also learn about the differences between scalp and source qEEG. Basic principles of teh creation of normas will be explained in order to pave the way for the on hands session on qEEG tools. 


Pedro Valdes-Sosa, University of Electronic Science and Technology
School of Life Sciences
Chengdu, Sichuan 

7. CBRAIN extensions for Quantitative EEG Analysis (qEEG): qEEGT and HarMNqEEG. The EEG normative project of the Global Brain Consortium

In this part of the course, we will teach how to operate with the Quantitative EEG analysis facilities integrated into the CBRAIN-LORIS ecosystem for Open Science. Data can be stored in LORIS-CBRAIN servers in several EEG formats, including the recent BIDS-EEG standard. Descriptive parameters for the qEEG can be calculated for the Log Spectra, and, for the first time, for the full cross-spectral matrices, at the scalp and the sources, both for the Narrow and Broad Bands spectral models. Age-dependent probabilistic Z-scores can be calculated versus the Cuban Normative Database (1990) and the Multinational Normative Database obtained under the EEG Normative Project of the global Brain Consortium, which comprises 1600 subjects from 5 to 90 years old from 9 countries. Procedures for homogenization (harmonization) of data registered with different EEG machines to make them more comparable, are also available through these tools. Users can also benefit from CBRAIN computing resources capabilities.  


Jorge Bosch Bayard, McGill University Montreal, AL 

8. Important research questions in clinical EEG: biomarkers and causality

Though there are many tools for the analysis of clinical data, the search for explanations of brain mechanisms and treatments relay ever more on the notions of causality and mediation. We explain briefly these concepts and the challenges of analyzing high-dimensional EEG datasets. The participants will get an insight into what types of relevant questions they may ask with Open Electrophysiology and will understand some examples of datasets to be used in the lecture on software tools for this purpose. The participants will understand how to use modern statistical methods to select biomarkers. 


Maria Bringas-Vega, University of Electronic Science and Technology of China Chengdu, Sichuan 

9. Causal inference in clinical neuroscience with open science EEG

Attendees will learn the statistical basis of causal inference and the recommendations for communicating causal inference results. Furthermore, a hands-on session of clinical study utilizing causal inference framework (e.g. mediation analysis) with qEEG will be provided. The particpants will be able to carry out and communicate this type of analysis after taking this course. 


Qing Wang, Shanghai Mental Health Center Shanghai, [Select a State]