I just worked on something similar at a hackathon this weekend with the Muse Headband, it was really fun. We used a support vector machine to maximize the "calmness" output of the band, which is some weighted sum of gamma and beta waves I believe.
Depends on what exactly you did, but the SVM doesn't maximize calmness - it finds a maximally separating hyperplane that separates calm data from not-calm data. If you had real-time biofeedback then the person wearing the handband might have used the feedback to help them relax, but in that case they are the one maximizing calmness, not the SVM.
Ok. My only experience with SVMs was reading the wikipedia article a year ago. Our implementation evolved pretty organically and I only thought later that it was like a SVM. thanks for the info.
Obviously irrelevant, which renders moot the other two questions that you asked me.
My point is that the work itself is what makes something scientific, not the workers' CVs (note the careful wording here; calling them researchers is another appeal to credentialism).
With that in mind, what would I like to see? You should be able to answer that one yourself now.
The hardest part of the weekend was getting real-time data through OSC. It's been done in Python and node before, but none of us were super proficient in getting data from the local server the scripts pushed to. This is the matlab library we eventually settled on: https://github.com/MuSAELab/muse_osc
I'd say it was pretty easy compared to Myo, but I wouldn't pay $300 for one. I'd use it again though if it was available for a weekend.
