Last year I bought an EEG headset (the Mindwave Mobile) to play with my Raspberry Pi and then ended up putting it down for a while. Luckily, this semester I started doing some more machine learning and decided to try it back out. I thought it might be possible to have it recognize when you dislike music and then switch the song on Pandora for you. This would be great for when you are working on something or moving around away from your computer.
So using the EEG headset, a Raspberry Pi, and a bluetooth module, I set to work on recording some data. I listened to a couple songs I liked and then a couple songs I didn’t like with labeled data. The Mindwave gives you the delta, theta, high alpha, low alpha, high beta, low beta, high gamma, and mid gamma brainwaves. It also approximates your attention level and meditation level using the FFT (Fast Fourier Transform) and gives you a skin contact signal level (with 0 being the best and 200 being the worst).
Since I know very little about brainwaves, I can’t make an educated decision on what changes to look at to detect this; that’s where machine learning comes in. I can use Bayesian Estimation to construct two multivariate Gaussian models, one that represents good music and one that represents bad music.
Each Friday is PiDay here at Adafruit! Be sure to check out our posts, tutorials and new Raspberry Pi related products. Adafruit has the largest and best selection of Raspberry Pi accessories and all the code & tutorials to get you up and running in no time!
Stop breadboarding and soldering – start making immediately! Adafruit’s Circuit Playground is jam-packed with LEDs, sensors, buttons, alligator clip pads and more. Build projects with Circuit Playground in a few minutes with the drag-and-drop MakeCode programming site, learn computer science using the CS Discoveries class on code.org, jump into CircuitPython to learn Python and hardware together, or even use Arduino IDE. Circuit Playground Express is the newest and best Circuit Playground board, with support for MakeCode, CircuitPython, and Arduino. It has a powerful processor, 10 NeoPixels, mini speaker, InfraRed receive and transmit, two buttons, a switch, 14 alligator clip pads, and lots of sensors: capacitive touch, IR proximity, temperature, light, motion and sound. A whole wide world of electronics and coding is waiting for you, and it fits in the palm of your hand.