I write this on the last day of the fellowship. With a really heavy heart. Eleven weeks went past really fast. Although I shall be back again in Ann Arbor for school in September, it won’t be the same. This was one of the best summers I’ve ever had. I will surely miss everyone at Backyard Brains!
In my last post, I mentioned about how I could perform post-hoc classification to determine whether a person is thinking about movement or not. For most of my time after that, I was working on having a better classification accuracy, by tweaking parameters here and there, collecting more data and validating the results. The average classification rate I achieved was approximately 88%. Which is very good. But, post-hoc classification has no use with respect to application. And so, I have started working on reading continuous data and classifying with a real time interface. But time decided to just fly as fast as it could. So I will definitely continue working on it through the next month. No other major updates about the project for today.
Meanwhile, we were all also preparing for the our poster presentation which was on 1st August. It was my first ever poster presentation, and it turned out to be so motivating and inspiring: looking at the amazing research by so many other students and getting feedback on our research, getting a chance to have a meaningful discussion about our work, all of it was so fruitful and fun.
One more thing which I realised is that I never really discussed why the behaviour of mu rhythms is the way that it is. In the sense, what is the reason why these particular waves disappear with movement or the thought of movement. This is something which should’ve been in the very first blog post, but I guess better late than never? So, there isn’t really a concrete explanation for the behaviour of mu rhythms, but of all the different theories, I came across one which personally to me made the most sense. Feel free to correct me if you feel so! As mentioned before, mu rhythms are most prominent when a person is physically at rest, to be specific when the neurons in sensorimotor region are ‘idling’. However, with the thought of movement or with actual movement, these neurons all start sharing a huge amount of information at the same time. Hence, a very high ‘information capacity’ results into a weak signal. This is similar to the stadium analogy that Greg often uses. When outside the stadium, we can never really figure out what’s going on inside because there are thousands of different voices at the same time. And thus we can never really know what is being said. On the other hand, when everyone is singing the national anthem, we can hear it outside because everyone is saying the exact same thing. Thus it makes sense that the mu rhythms are stronger when all the neurons are in the exact same ‘idling’ state, and they get suppressed with the onset of movement or movement visualisation because they are all firing at the same time and sharing a ton of information. Here’s an image to visualise all that I wrote:
Again, this explanation might not be the correct one, it just made sense to me personally.
And with this I conclude. I hope to be able to write again for all of you with further advancements in my project. I would like to thank Greg and everyone else at Backyard Brains for this amazing summer! Feel free to reach out to me (email@example.com) with any further questions and discussions!