This week I was working on the REU presentation video, getting feedback from my mentor and the PhD student I work with in order to do a concise/coherent product. I started the week by plotting curves with the correlation values between Amplitude Coupling and Phase Coupling states based on the optimal alignment of temporal profiles per each subject. We expected these curves to look like a bell-curve. I tried normalizing the curves to plot them with the temporal profile averages. The correlation curves of z-scored data weren’t interpretable since the values are insignificant. On the other hand, the curves of non z-scored data don’t showed a noticeable variance, so I went on to perform a two-way ANOVA test to test the interaction between the Amplitude Coupling and Phase Coupling Temporal Profiles. There was indeed a significant interaction between AmpC and PhC. I then plotted the non z-scored spatial correlations (y-axis data) using the pairs of states extracted from the temporally-matched states of z-scored data.

Finally, I started working on a second project focused on Education. This revolved around trying to predict user behavior on a ClassTranscribe, a web-based platform that provides lecture videos that are CC’ed and have searchable content. I tried to figure out how to handle the data from ClassTranscribe. I had a big issue with memory since the data set is so big. I tried a few things (breaking the data into chunks, filtering it, and finally, I upgraded my python from 32 bit to 64 bit). This took out most of the time, however, now I can access the data well and will be doing more progress to extract relevant features.