Toolboxes Bouquet

During our session, we will present a recently published analysis pipeline for Magnetoencephalography (MEG) called FLUX. There are several open-source toolboxes developed by the community, each providing a wealth of options for analyses. This poses a challenge for reproducible research as well as for researchers new to the field. The FLUX pipeline aims to help making the analyses steps and setting explicit for standard analysis using two of the most popular toolboxes: MNE-Python and FieldTrip. The FLUX pipeline goes from pre-processing to source localization of oscillatory brain activity, but it can also be used for event-related fields and multivariate pattern analysis. The pipeline includes documented code for MNE-Python and FieldTrip. During the session, we will use a sample data set on visuospatial attention to illustrate the analysis steps. We will provide all the scripts as interactive notebooks implemented in Jupyter Notebook and MATLAB Live Editor. We will also provide explanations, justifications, and graphical outputs for the essential steps. Furthermore, we will suggest text and parameter settings to be used in pre-registrations and publications. We believe that this session will help new researchers entering the MEG field by providing them with some standardization on the basic analysis steps, as well as researches who want to align their approaches across different toolboxes.

Prerequisite: None

See tutorial discussion forum

Frites is a Python toolbox for task-related functional connectivity analysis based on information theoretical approaches and group-level statistics on neurophysiological data (M/EEG, Intracranial). Frites also contains workflows accessible to users with little programming knowledge. For more information please see the Frites webpage (https://brainets.github.io/frites/) and recent releases on github (https://github.com/brainets/frites).

Prerequisite: Python

See tutorial discussion forum

Electroencephalography (EEG) is a popular method to measure brain activity, with a large and increasing number of analysis techniques available to interpret the recorded data. One difficulty in developing and evaluating such analysis techniques is that no ground truth is available that describes the actual source-level activity of the brain. As such, developers must use other ways to examine the validity of their EEG analysis approaches. In particular, researchers have used simulated EEG to achieve this. Simulating EEG activity allows the results of analysis techniques to be compared to the ground-truth initial parameters of the simulation. The MATLAB-based EEG simulation toolbox SEREEGA (Simulating Event-Related EEG Activity) provides functionality that covers the majority of EEG simulation approaches used in past literature in a dedicated set of scripts (and a GUI). This free and open-source toolbox supports different head models, and allows source-level activity to be simulated using, inter alia, oscillations, event-related spectral perturbations, event- related potentials, noise, and auto-regressive models. The session will introduce SEREEGA to the audience, providing a step-by-step guide to simulating event-related EEG activity with a fully known ground truth. This session will also cover the newly released HArtMuT headmodel that can be used with SEREEGA to simulate signals coming from non-brain sources, a factor that has long been difficult but is required for the realistic simulation of artefacts.

Prerequisite: MATLAB, EEGLAB

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This workshop will combine a short theoretical introduction with a hands-on tutorial to the “unfold toolbox” (http://www.unfoldtoolbox.org), a new framework for analyzing M/EEG data recorded in naturalistic paradigms (e.g., mobile brain-body imaging) or other experimental settings that involve overlapping brain responses from different events (e.g., fast multimodal stimulation, stimulus- and response-related potentials, microsaccades). It also allows the researcher to control for influences of linear or nonlinear nuisance variables on the M/EEG signal.

Prerequisite: To take part in the hands-on part, you need a computer with Matlab installed (MATLAB 2016a or newer). You will also need a version of EEGLAB, but this will be provided by us if not already available. You do not need to install the unfold toolbox before the workshop. Some familiarity with Matlab is helpful but not required.

If you want to prepare in advance, you can use these materials:
Toolbox paper: https://peerj.com/articles/7838/
Tutorial paper on applications: https://jov.arvojournals.org/article.aspx?articleid=2772164

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OpenViBE is an open-source software platform dedicated to designing, testing and using brain-computer interfaces (BCIs). It can be used to acquire, filter, process, classify and visualize brain signals in real time, with applications in the medical field (assistance to disabled persons, helping physical rehabilitation, neurofeedback), but also in virtual reality, video gaming and robotics.
It is compatible with a large selection of EEG hardware devices, and features a wide array of signal processing algorithms and machine learning methods. It supports scripting through Python and MATLAB, and provides an easy-to-use GUI allowing to create and manipulate pipelines or specific use-case scenarios, thanks to a flexible architecture based on independent processing boxes communicating with one another.
During the first part of the session, a general overview of the BCI context and of the software will be given. We will also present several examples of use of OpenViBE in the BCI domain.
In the second part, we will show a step-by-step tutorial showing the different capabilities of OpenViBE, using a Motor Imagery experimental protocol. Different scenarios will be analyzed in details to showcase typical operations: signal acquisition, extraction of features of interest, training and running a classification algorithm.
Finally, in the third part, we will discuss more advanced topics such as external interfaces (i.e. using the widely used Lab Stream Layer transfer protocol), developing C++ boxes, using Python for scripting, and using OpenViBE as a processing engine. To illustrate this, a new application currently in development will be demoed, showing how to push things further, and allow using BCI in clinical and research contexts.

Prerequisite: None

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Some of the main challenges when analyzing infant EEG is that recordings are short and typically heavily contaminated with artifacts. Additionally, the signal features change along development, and variability across participants is higher than in adult data. APICE (Fló et al. 2022) is a set of functions based on EEGLAB and written in MATLAB specifically developed to deal with these issues while keeping the pipeline fully automated. One of the main contributions of APICE is that it provides multiple algorithms for motion artifact detection using adaptive thresholds. The implementation of adaptive thresholds makes it suitable across age groups without requiring manual rejection or substantial threshold adjustments for each protocol resulting in better data recovery than other available pipelines. Moreover, APICE works on continuous data, which makes it suitable for different types of analysis. APICE also provides functions to define non-functional channels and data segments to reject, correct localized artifacts, and apply ICA using iMARA to identify components associated with physiological artifacts. APICE is fully automated, flexible, and modular; thus, it can be easily combined with other toolboxes or preprocessing steps.
In the first part of the talk (~45 minutes), I will briefly overview the challenges of infant EEG preprocessing and introduce APICE. More specifically, I will show its principles and explain the different types of functions and pipeline architecture. The second part of the talk (~45 minutes) will be a hands-on tutorial. I will provide some example scripts and recordings to preprocess. I will also explain how different parameters and steps can be adapted for different requirements.

Prerequisite: MATLAB, EEGLAB toolbox (recommend version >=14.0). To have downloaded APICE (https://github.com/neurokidslab/eeg_preprocessing) and iMARA (https://github.com/Ira-marriott/iMARA/). If participants desire to preprocess their data during the hands-on, the data should be already in the EEGLAB format (.set, .fdt) and have the layout of the electrodes.

See tutorial discussion forum