One clue as to how we feel while listening to music is in how we move, from dance-like motions to brows furrowed in concentration. Surface electromyography sensors (sEMG) are very useful in picking up muscle contractions which generate these expressive behaviours. In the solo response project, two typical sensors on the face to capture brow furrowing and smiling (see the set-up post) and an additional sensor tracking contractions of the trapezius muscle up the back of my neck, to capture tension in the back and head nodding. While there is much to say about each signal, this post is about what these sensors measured of my responses to Varuo, by Sigur Ros.
In the analysis post, I mentioned that the B section, starting around 220s, was really intense, and often overwhelming. These sensors agree with that, all shwing more instances of high muscle activity across sessions from 250s to 350s. Sublime face, yawning, and an explanation of how to read this graph below the cut.
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Note: music review posts discuss the stimulus selection, structure, and general response patterns, giving an overview of the stimulus before digging into the responses.
Stimulus number 1, numbered for convenience not playlist order–each session’s music was randomly ordered–was a track from 2012 Sigur Rós album Valtari. I’d been enjoying the band’s previous work in the last year, taken by the textures, slow builds, and dramatic extremes of intensity. When I saw a new album had been release a month or two before the scheduled response recordings, I chose a track to reserve for the experiment. The 24 response session are my first 24 full listenings to this epic piece of music, and it was a really intense experience almost every time. Read the rest of this entry »
I’ve finally got a version of these responses in a format I’m willing to post. The responses, organised by stimulus as matlab data structures, are being added now on figshare. I’m also planning on releasing them in a more flexible format, but I’mnot yet decided how (suggestions welcome). In its current format, the responses and all interesting extracted features accumulate to nearly 3 gigs of numbers, this isn’t a data pile for the faint of heart.
In my own files, I have other structurings of the data, specifically sensor-wise structures which allow for between stimulus comparisons and downsampled versions to look at everything at once, but these are not necessarily in sharable good shape.
(This is a repost from April 2013, as I was preparing to bring this experimental data to conferences)
In the coming months, I’ll be taking results from the solo response project to several conferences, and reviewer feedback has me worried about people dismissing this data because I collected the data from myself. I keep getting distracted by these imaginary confrontations with suspicious researchers so it’s time I lay down some concisely-expressed arguments to appease the hypothetical skeptics.
Problem 1: One subject = bad empirical research
I don’t like this one because the premise is wrong, but I’ve gotten it a lot already, so here goes. Read the rest of this entry »
(This is a repost from during the recording sessions in July 2012)
How else does a grad student get 40+ hours of experimentation time over 20 sessions from one human subject?
I thought someone might have an issue with my idea of using myself, but apparently not. The NYU ethics review office didn’t even want to hear about what responses I’d be recording, or what protocols I’d be following. Maybe they assumed that music is a harmless stimulus, or that I knew I wasn’t allergic to the glue we use to attached electrodes, or that I was very unlikely to sue the school because of research I’d planed myself. I hope they didn’t care because the data collection is happening at another institution, in collaboration with someone who’s standing ethics certificate does cover this type of experiment.