Presenter(s): Ryan Leriche—Biology
Faculty Mentor(s): Nicole Swann
Session: Prerecorded Poster Presentation
With no previous signal processing background, I began studying how electrical brain waves vary with movement speed and uncertainty . When applying all that I read, I learned when to flesh the details or just see the big picture .
My lab uses scalp-electroencephalography (EEG) to record brain activity . EEG data can be noisy,
but there are methods to see through this noise . After some pre-processing, I ran an independent component analysis to decompose a complex signal into its sub-signals . I removed the eye movement sub-signals as I just was interested in brain activity . With kurtosis values—the sharpness of a signal—I could remove artifactual trials .
I was uncomfortable using ICA and kurtosis measures without knowing exactly how they worked . Learning every nuance would have halted my analysis progression . So, with a conceptual understanding, I used these tools from the EEGLAB Toolbox for MATLAB to generate a cleaner EEG signal .
With a clean signal, I began my time-frequency analysis . This would describe how well a sine wave at a given frequency represents my signal . I could not get a conceptual hold on this topic . After pausing my analysis and taking an online course—at my PI’s suggestion—my progress accelerated .
I now could examine how electrical brain activity changes with movement uncertainty and speed . My analysis suggests that brain activity increases with slower movements; however, now I need to learn how to statistically verify this result .
Looks like I need to continue reading methodology papers and MATLAB/EEGLAB documentation .