Jean’s Status Report for 02/19/2022

This week my main focus is looking into the signal algorithm and process design. At first I was thinking of using the signal processing application. I was looking deeply into using the BCI2000(widely used BCI signal processing platform) and BCIlab(real-time MATLAB extension). However, both wouldn’t support our headset model and we would run into the problem of connecting the  EMOTIV data acquisition app, the processing app and the interface app together. After finding out that the EMOTIV API can obtain the data directly from the device with all the classes and structs defined. I changed my idea to using python (in which EMOTIV API is written in) and will do all the steps of signal processing from there instead. I was trying out the headset trials with my friends a couple of time but it seems that I may not be a good subject since my data is very noisy. Thus, that is one thing we may have to explore later if things would be fixed after we changed to the new set of electrodes that we have just ordered. This week I have read a lot of research papers and about neural signal processing techniques. In our design, I planned to train a model with a lot of collected datasets. We will also have to apply our bandpass filtering to detect certain brain waves, like beta and alpha that may be useful information to validate the data and the user activity. I have read on the paper that some were using tongue movement for the experimentation and when we tried, we found out that it could be a potential control data. I also read more about machine learning algorithms after Jonathan’s suggestion on using the random forest. I found that support vector machine(SVP) and neural network is a good option too. Though, I will go meet up with a neuroengineering PhD that I know for advice on our choices and review my design.