Following our series of CIMCYC Sessions, we are pleased to announce our next Time-resolved Neuroimaging (EEG+) session which will be held by David López-García, who is going to talk about The MVPAlab Toolbox, a tool for multivariate analysis of EEG data. Designed with an intuitive graphical interface, MVPAlab allows both inexperienced and experienced users to perform complex analyses based on machine learning techniques, such as temporal decoding.
You can easily access the GitHub repository where this tool is available for downloading: https://github.com/dlopezg/mvpalab.The reference to the paper where this tool has been published can be found below in case you want to read it in advance.
Reference: López-García, D., Peñalver, J. M. G., Górriz, J. M., & Ruz, M. (2022). MVPAlab: A machine learning decoding toolbox for multidimensional electroencephalography data. Computer methods and programs in biomedicine, 214, 106549. https://doi.org/10.1016/j.cmpb.2021.106549
There is an available podcast for you to have a general idea of the topic: https://drive.google.com/file/d/166tSPJnSDmOgxBjrhHJXPLoSz7OrHR4h/view?usp=sharing
Proposed questions to reflect on
- Considering MVPAlab requires proprietary MATLAB, how does this affect its accessibility and potential user base compared to similar open-source toolboxes for M/EEG analysis?
- Based on the limitations and future plans discussed, which MVPAlab development (e.g., mitigating overfitting, adding more algorithms, integrating BIDS) do you see as most crucial for its impact in neuroimaging research?
It will certainly be an excellent occasion to share different impressions and reflect on insightful approaches regarding the main topic. No prior experience in EEG is needed —just curiosity and a willingness to discuss ideas. Save the date!
The session will be held in Spanish.
Date: Thursday, 8th May 2025
Location: Seminario 4 - CIMCYC
Time: 10.00 am
Join us online too!: https://meet.google.com/sgz-quvr-rma
Upcoming CIMCYC Session:
- Thursday, May 22nd, 2025 - AI, Mind and Brain: A large-scale examination of inductive biases shaping high-level visual representation in brains and machines (Concrete example with implications for the family of DNNs)