Take it from Twitter: Low-cost EEG is a powerful teaching tool!
As a business, it can be strange to package and ship off all these different neuroscience education tools, wondering if they’ll like their new home, will they make a difference in this big, strange world?
Much like a proud parent, we are always excited when we see tweets and testimonials like this one from an international user in Ireland, preparing to use one of our DIY EEG devices for neuroscience outreach!
Not just that, but it inspired a fun follow-up conversation amongst other Twitter users:
We’ve got history, humor, and enthusiastic recommendations all in one!
Brief History of EEG
What John and Mark are referencing is Hans Berger’s pioneering work in brain recordings, the results of which were published in 1929: see the publication here in its full, German language glory!
Wikipedia provides a nice summary of this original experiment:
His method involved inserting silver wires under the patient’s scalp, one at the front of the head and one at the back. Later he used silver foil electrodes attached to the head by a rubber bandage. As a recording device, he first used the Lippmann’s capillary electrometer, but results were disappointing. He then switched to the string galvanometer and later to a double-coil Siemens recording galvanometer, which allowed him to record electrical voltages as small as one ten thousandths of a volt. The resulting output, up to three seconds in duration, was then photographed by an assistant.
The original recording from that string galvanometer is pictured in John Butler’s tweet!
Fortunately, 90 years has advanced technology considerably, allowing us to perform the same experiment and view the same results with our non-invasive EEG Sweatband!
Much less intimidating than inserting silver wires under the scalp!
The Tools to Make it Possible
Bring this EEG experiment and demonstration to your classroom!
This University of Michigan student team developed a way to control a drone with a new kind of controller…
We work with students of all ages — from outreach to early elementary, to hands-on demonstrations, labs, and even research with students from fifth grade to… well, grad school and beyond!
We wanted to share this novel and exciting project which is the result of a group of Aerospace Engineering students who had an exciting question: Can we fly a plane, or at least a drone, with our thoughts?
It wasn’t an easy project, but with very minimal support on our end, they were able to get a prototype up and running within just the few weeks allotted to the project!
But how does it work?
The students took advantage of two signals that you can record using the Heart and Brain SpikerBox – First, EEG (Electroencephalograms, or brain waves) could be used to “wake up” the drone (take off / ready) by opening your eyes, or “put it to sleep” (land / standby) by closing your eyes. This works because, when you record from your occipital lobe, alpha waves are present when your eyes are closed, and “disappear” when they are open – a phenomenon which the students leveraged for their “On/Off” switch.
Then they used EOG signals (Electrooculograms, from your eyes!) to tell the drone to move in different directions depending on if you are looking up, down, left, or right. This is possible thanks to the different electrical signals recorded when you look in different directions.
They were able to do this in real time, creating a very creative control scheme that could be applied to other devices as well. The sky is the limit for the future of this project! Or maybe not just the sky… maybe space isn’t even a limit anymore for students these days!
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!