Updated version of the Activity Analysis toolbox is up on GitHub now. The changes in functions came from working with more psychophysiological measurements and events. Now there is an option to assess the coordination score for a response collection using the distributions of local rank in activity levels, instead of the distribution of activity levels themselves. For more on Activity Analysis generally, check out this post on the paper that was published earlier this year, including a link to the full text pdf.
This addition may seem like a tiny adjustment. For many kinds of response events, it doesn’t substantially change alignment assessments. When considering the coordination of events that are fairly consistent in rate over time, like say inspiration onsets, the distributions of activity levels and of local rank give the same kinds of coordination scores. However, for response events that change their rate of occurance over the course of a piece of music, like skin conductance increases, the activity level time series distribution obscures moments of exceptional alignment in quieter times. In such cases, the local rank does a better job at capturing anomalous alignments.
This rank based coordination score appeared to be necessary when I was testing unrelated response collections of facial sEMG signals and skin conductance. The old calculation generated scores that were too low, producing insufficient numbers of false positives, while this adjustment behaved just as the statistic should.
Besides this change to the localActivityTest function outputs, a few other functions have been tweaked and the demos have been amended to work with these changes. Lastly, Demo_2 now include coordination assessments on a number psychophysiological signals recorded during 24 listenings to a fun piece of music by a single participant.
It all should work just fine in MatLab.
I’m looking forward to releasing this version in Python in the not too distant future too.