Quantified Brain and Music for Self-Tuning
Project Infobox 
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| Self researcher(s) | Rocio Chongtay |
| Related tools | brainwave equalizer, Neurosky MindSet |
| Related topics | Mood and emotion, Brain activity, Music |
Builds on project(s) |
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| Has inspired | Projects (0) |
| Show and Tell Talk Infobox | |
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| Featured image | 
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| Date | 2014/07/18 |
| Event name | 2015 QS Global Conference |
| Slides | Quantified-brain-and-music-for-selftuning.pdf |
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Quantified Brain and Music for Self-Tuning is a Show & Tell talk by Rocio Chongtay that has been imported from the Quantified Self Show & Tell library.The talk was given on 2014/07/18 and is about Mood and emotion, Brain activity, and Music.
Description[edit | edit source]
A description of this project as introduced by Quantified Self follows:
In this talk, Rocio Chongtay shares her novel and thoughtfully designed experiments in using music to adjust her concentration and relaxation depending on what she’s doing. Using a consumer EEG device from Neurosky, Rocio tried different types of music while tracking the relaxation and concentration dimensions identified by the Neurosky algorithm. She talks about the experiences she had with Neurosky in her lab, and how she turned those techniques to understanding something about her own mind.
Video and transcript[edit | edit source]
A transcript of this talk is below:
Rocio Chongtay “Quantified Brain and Music for Self-Tuning”
My name is Rocio Chongtay, I work at the University of Southern Denmark and today I'm here to talk about the quantified brain for music and self-tuning. To give you a little bit about context and music, while I was studying for my final examinations for the master degree, I released I had problems concentrating when there was noise around, when there was total silence or when I was listening to music with lyrics or catchy beats. I also realised that a certain type of music actually helped me to concentrate, so more like a pleasant background noise that would cancel all the outside distractions, for example, music without lyrics or the catchy beats. At the moment I'm working with brain-computer interfaces or BCI, very much a single dry sensor that the user just put the sensor and touches the forehead, and this sensor can record some brain activity, basically eight different brainwaves and the blinking and it can also give some indicators for concentration and relaxation. With this device, we build a brain-controlled artifact in which we use the brainwaves to control how fast the speed. So the device connects wirelessly via Bluetooth to a computer and the computer controls how fast the fan goes. So it's configured so the person that is wearing this, the higher the concentration the faster the fan goes and then the beach ball with the shape of the world is lifted. I was very surprised that many people have problems concentrating and they were surprised that they couldn’t concentrate on command on a moment to another, of course, it was a very noisy environment so it was understandable. So as I found out certain music helps me to concentrate while reading or working, I also found out that other types of music helps me to tune myself for best performance in other activities like programming or sports. The problem is how can I find the playlist for other activities other than trial and error and it would be a very time-consuming activity. so I decided to use the brain-computer interface to quantify my levels of concentration and relaxation while I was listening to music and when I was performing to different activities To do this I used a program called Brainwave Visualizer, by Neurosky, the same company that makes these devices, and this program is integrated with iTunes, so it allows to record the different brain waves and the different indicators of the concentration and relaxation while listening to different types of music. I’m going to call them meditation that they call relaxation because meditation can have many other meanings, so I will use these terms here. The settings for the recordings were as follows, but before that, I'm going to show you some snapshots of single moments where I was recording songs. In this case, it's Hotel Intro by Moby, with a high level of concentration and low level of relaxation. Another example, the same album, Hotel, the song’s beautiful and with good levels of attention or concentration and relaxation. The third example is still the same album by Moby and the song is It’s Raining Again. And in this example is high levels of concentration and low levels of relaxation. These examples are showing you a snapshot, a single moment on a song. So in this graph I’m showing you a full recording of one song, the Hotel Intro and the blue line is representing relaxation and the red line is representing the levels of concentration. So you can see here the trend is what's more dominant. In this case, you can see a trend for a higher levels of relaxation than levels of concentration. So now I can tell you about the settings of the recording. I decided that I will have two hours of recordings in the morning when I need to work very concentrated and in the first hour I was recording the album Moby and in the second I would just chose a selection of classical music. I also recorded two hours in the afternoon when I was more tired and then I can do more easy reading or programming and then I inverted the order and put in the classical music first and then the Moby album as second. Having done that I can show you some full examples of songs, this one playing in the first hour; one of the Moby songs. It shows and a tendency of higher levels of concentration than relaxation. In the next graph I will show you one of the classical music played on the second hour, and in this case you can see, especially on the second half, high levels of relaxation compared to the concentration. On the last graph there is a quite visible dominant at the beginning, highest levels of concentration comparing with relaxation, and there is a mark for concentration and a high level of relaxation and that was because I was actually falling asleep. But I also needed to find myself tuning tunes for other activities like archery. In archery, in order for a good performance I need to be highly concentrated, and I need to have some level of relaxation, but not too relaxed. So after I had found my self-tuning tunes, the best that has work for me is for highly concentrated reading or working, a selection of classical music, even commercial series like music for reading works. For archery, so far the best album for me is the Hotel album by Moby. And for programming it’s actually almost any high beat collection works well. Now, I’m going to show you some examples of what I believe is self-tuning effects for archery. Here, I’m shooting 18 meters and without music, the arrows are all a bit off center, and when I put music on the arrows get more into the target. Another example is me shooting at 70 meters. The arrows without music are a bit more spread all over the target, and when I put the music on, they get more closer together and more closer to the center. So once I found my ideal self-tuning tunes, I actually found out that by just playing the tunes, it feels easier to get into the actual mindset that I need for this specific activity. And this is what I’m calling self-tuning. So to finish with my talk, I’m going to leave you with this quote by Oliver Sacks, who was a British neurologist and who believed that the brain is the most incredible thing in the universe.
Thank you for your attention.
About the presenter[edit | edit source]
Rocio Chongtay gave this talk.