Computational Processing of Brain Data

• 

  Dare to brighten-up

Inspiration

Mental well-being is becoming an issue of primary importance. Our work processes and analyzes medical data for characterising potential biomarkers with the long term goal of better treatment and diagnosis of mental disorders so as to aid clinicians and advance medicine.

What it does

Our work processes electrophysiological data measured from the scalp (EEG) so as to compute where in the brain that activity originates from, which is also known as source localization, by using Python.

How I built it

I used the Python library. I had to go through documentation and forums and reach out to mentors as it was not well-documented and I was new to Python.

Challenges I ran into

I was new to Python. So, it was a challenge to catch up on that and troubleshoot the related bugs that I ran into while implementing it on my dataset. Also, this library's documentation was very poor as it's still in the research level. So, it took longer and more work to resolve issues. Furthermore, the final steps require learning another toolbox that is integrated with this library and hence, had to be reserved for future scope.

Accomplishments that I'm proud of

I made a leap of progress in gaining Python skills and knowledge esp. troubleshooting and implementing the related libraries. I was able to preprocess my EEG data and successfully remove the artifacts. I networked with many like-minded, hard working, young enthusiasts and feel excited and rejuvenated for my work like never before.

What I learned

I learned that reaching out to people is essential. I learned the necessary Python skills as well as Arduino during the workshops. One should never give up. I am capable of more than I could think of.

What's next for Computational Processing of Medical Data

The next steps would be to integrate the new toolbox to the existing python library for yielding results that can extend this application to wider network analysis.

Built With

• python