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The goal of this project is to create online resources for helping students or researchers transition towards the use of open and reproducible techniques for analyzing M/i/EEG data. As the culture around open* is gaining momentum, an increasing number of researchers in the field of electro- / magneto-encephalography are transitioning from close- to open-source techniques. However, this transition can be challenging, especially if it is not explicitly encouraged in one’s lab or institute culture, and because very often, online resources are sparse and have been originally developed for other fields.
During the Brainhack we can use openly shared datasets and start by exploring and comparing existing toolboxes for analysing M/EEG data in python or R, identify strengths and weakness for each of them for different types of analyses. While working on these analyses we can prepare Python or R jupyter notebooks that can be eventually assembled as a tutorial for analyzing M/i/EEG data.
Ultimately, we could work towards proposing this as an additional workshop for the data carpentry , which provides workshop materials for computational research in various domains, such as bioinformatics or social science, but so far have no materials for neuroscience.
Find out what we're working on on the project's repo!
The text was updated successfully, but these errors were encountered:
Submitted by Athina Tzovara*
The goal of this project is to create online resources for helping students or researchers transition towards the use of open and reproducible techniques for analyzing M/i/EEG data. As the culture around open* is gaining momentum, an increasing number of researchers in the field of electro- / magneto-encephalography are transitioning from close- to open-source techniques. However, this transition can be challenging, especially if it is not explicitly encouraged in one’s lab or institute culture, and because very often, online resources are sparse and have been originally developed for other fields.
During the Brainhack we can use openly shared datasets and start by exploring and comparing existing toolboxes for analysing M/EEG data in python or R, identify strengths and weakness for each of them for different types of analyses. While working on these analyses we can prepare Python or R jupyter notebooks that can be eventually assembled as a tutorial for analyzing M/i/EEG data.
Ultimately, we could work towards proposing this as an additional workshop for the data carpentry , which provides workshop materials for computational research in various domains, such as bioinformatics or social science, but so far have no materials for neuroscience.
Find out what we're working on on the project's repo!
The text was updated successfully, but these errors were encountered: