I have analyzed various biomedical imaging data, and given the specific needs of each application, usually needed to develop an in-house analysis tool. They included PET scan data, fMRI data, Electrophysiology, Electroencephalography (EEG), and Eye-tracking data. Some of the analysis tools became so comprehensive that I decided to publish them as open-source packages, hopefully, they will be of use to other neuroscience enthusiasts. Below, you can find a brief description of each.
Molecular Functional MRI

Functional MRI or fMRI is a non-invasive medical imaging technique that has been widely used to study the brain. The most common signal used in fMRI measurements is BOLD (Blood-Oxygen-Level-Dependent). But there are also other possibilities. In this paper published in PNAS, we have reported the use of a molecule specifically designed to bind with calcium, and we have shown how it can be used to record the changes in calcium concentration in the brain. 
As part of this collaboration, I developed a python library specifically to analyse molecular fMRI signals. I published it in the Journal of Open-Source Software (JOSS). You can find the publish repository here

EEG (electroencephalogram) is a non-invasive medical imaging method. EEG can be used mainly to record brain activity in the context, not the so-called deeper regions, which is a major disadvantage of this method compared to fMRI. But its temporal resolution is usually quite high (of the order of milliseconds), compare to fMRI which has a temporal resolution of typically seconds or tens of seconds. It has been used to diagnose mental diseases since the 1930s and it has proven quite effective in capturing various cognitive functions. 
The most common library to preprocess, analyse and visualise EEG signals in Python (my language of choice) is MNE. If you want to use a simple Graphical User-Interface for MNE, you can use MNELAB (I was among the reviewers who reviewed the code and accepted it for publication in JOSS).
As part of my own EEG analysis pipeline, related to this and this project, I developed my own method to analyse EEG signals in the frequency domain that I call it Spectral Signature method. To implement it, I have written a Python library called MiTARES that can be found here


You look at a painting or a photograph. Or just walking around in a supermarket and your gaze wanders around the shelves and the aisles. Ever wondered how you look at a given scene in front of your eyes? In what order do you look at a painting? What items on the shelves in the supermarket catch your attention and why some are so hard to find? In order to answer such questions, a category of devices is used by neuroscientists, which are called eye-tracking devices. Some are stationary, which means they measure the "gaze behaviour" when we look at a screen or monitor. The other types are wearable eye trackers, which can be used to study gaze behaviour in a more natural setting. And I have worked with both. 
Also, in order to analyse the gaze behaviour for my project, explained here, I have developed a Python library called MiTgaze which you can find here
Behavioural Data

MiTABLE is a python library for Adaptive Behavioural Learning Experiments and you can find it here. I used it to analyse the behavioural data in which we recorded responses from rats when they were trained in a go/no-go paradigm. More info on those experiments can be found here
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