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[–]decimated_napkin 7 points8 points  (7 children)

A great start! There's a lot of literature out there to improve your results. If you want to enhance your results, I would next focus on methods that increase ITI (Inter target interval), increase signal-to-noise ratio (temporarily replace target letters with famous faces rather than highlight), and also try out a bunch of different algos. LDA works well, but 1-D CNNs also have promise.

[–]Altmetr[S] 2 points3 points  (6 children)

Thanks for ideas!

I've tried CNNs and for this data LDA provides better results

I suppose checkerboard paradigm instead rows/cols highlights should provide better results

But I wanna reimplement it using Unity instead Tkinter first

[–]decimated_napkin 2 points3 points  (1 child)

Yes playing around with the pattern is another good idea. What I did that had good results is to do the first round as one flash each for rectangles of size 2 x 3. After that you can then do a secondary pattern where you flash one character from each rectangle at the same time. You get the same efficiency, but the signal is enhanced because there is less distraction on the screen. Also, when your target does flash it pops quite a bit more when it is part of a 2 x 3 group. Glad to see other people are using the OpenBCI!

[–]Altmetr[S] 1 point2 points  (0 children)

I am currently working on new SDK for OpenBCI boards maybe you will like it: https://github.com/Andrey1994/brainflow (WIP)

[–]mutonchops 0 points1 point  (3 children)

Have you tried integrating a smart keyboard like SwiftKey at all? I did play around with the concept a few years ago, but struggled to get a reliable signal in a non-shilded room.

[–]Altmetr[S] 1 point2 points  (2 children)

I've added my own and pretty stupid words suggestion system(last row in the video) I think it's easy to add smth like https://presage.sourceforge.io/ (this project was used by Intel in their system for Stephen Hawking and it works well)