This week I recorded the dataset for training and testing with Chris. For each activity we recorded 3 episodes with 15-30 seconds duration. The activities include closed-eyes neutral, open-eyes neutral, think about lifting left arms, think about lifting right arms, move eyes from left to right, move eyes from right to left, smile and stare.
I also exported the recorded dataset to csv file and wrote a python script to automatically convert the batch of csv files to pandas dataset, which can be used directly for learning with pytorch.
One challenge with preprocessing the raw data is eye blinking removal. The artifact is visually obvious in the frontal lobe sensors AF3 and AF4. Using the MNE-Python neurophysiological data processing package, I was able to remove the eye blinking artifact using fast independent component analysis (ICA). This method works by separating the additive components of a mixed signal. In our case, the eye blinking is an additive component to our source brain signal. The fast ICA works by first pre-whiten the input signal (centering each component to have zero expected value, and then using a linear transformation to make the components uncorrelated and have unit variance). Then the fast ICA uses an iterative method to find an orthogonal projection that rotates the whitened signal such that non-Gaussianity is maximized. It is noteworthy that while the MNE-Python works well for preprocessing our recorded data, for real-time packages of short duration, its performance is subpar. So I am implementing the software for using morphological component analysis on the short time fourier transform to remove the blinking noise in real time, following the method suggested by
“Real time eye blink noise removal from EEG signals using morphological component analysis” by Matiko et al.
