Blogpost: 8th week of GSoC (Jul 15 - Jul 21)

JoseAlanis
Published: 07/22/2019

With this week, the second month of GSoC also comes to an end. It feels like a good moment for providing a summary of the progress made so far and also an outlook, considering the tasks and challenges that remain ahead.

Building MNE-LIMO, a "workbench" for linear regresssion analyis in MNE-Python

At the beginning of this GSoC project we set the primary goal of extending the functionality of the linear regression module in MNE-Python. During the last two months, I've focussed my work on developing functions, example code, and methodological tutorials with the purpose of providing MNE-users with a learning-oriented environment, or "workbench", for specifying and fitting linear regression models for experimental designs that are commonly used by the neuroscience community.

Here, we have achieve a some important milestones, such as showing how the python package scikit-learn can be used to fit linear-regression models on neural time-series data and how MNE-Python can be used in interface with the output of these tools to compute and visualize measures of inference and quality of the model's results.

In addition, we have recently launched a website, MNE-LIMO (short for MNE-LInear Regression MOdel), where we provide more background information and details on these tools and methods.

Bringing MNE-LIMO to the next level.

The challenge for the next month is to extend the tools and methods we've developed so far to allow the estimation of linear regression effects over multiple subjects. The goal here is to use a mass-univariate analysis approach, where the analysis methods not only focus on a average data for a few recording sites, but rather take the full data space into account.

This approach also represents the greatest challenge of the project as it rises a series of implications for further analyses and assesment of the results, such as controlling for an increased number of potential false positive results due to multiple testing.

In the following weeks I'll focus on adapting the tutorials and examples developed during the last few weeks to allow this kind of analyses.

 

 

 

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