This Is Your Brain On Bike
sports & fitness | stress
"This is your brain on bike," said Arlene Ducao, a computer programmer and digital animator at the MIT Media Lab's Information Ecology group showing off a map of city streets studded with colorful dots. Ducao is the inventors of MindRider, the bike helmet that reads its riders' brainwaves (hacked with an EEG sensor that displays the wearer's stress levels). The helmet also correlates the bikers' mental state with their geographical routes, creating maps of what Ducao calls the city's "psychogeography."
EEG (Electroencephalography) | MindRider | Wii Fit
So I came to Quantified Self though Wii fit. I’m still quite devoted user of Wii Fit. So these are some me’s of me as I’m going through training – this is my favorite Wii fit game. Maybe some of you have played it. The one where you flap your arms for some exercise.
And so you know going back six or seven years is when I really started to get into tracking my own self, but in a very limited, very contained way and you know this was in my house tracking things like you know, relative balance, my weight, and also my arm flaps.
At the same time I was working on some non-Quantified Self projects. This one is called – it’s a little bit difficult to see. It’s called Lumenhattio, and basically it’s a stretchy bike helmet cover. I ride my bike a lot and my bike helmet had become really dirty, so I started putting things on it and friends started encouraging me to trick it out. And I started moving my bike lights off my bike which had been stolen numerous times to my bike helmet. So that was how I started working on just a very simple stretchy bike helmet cover.
So this is me and a few other people wearing some different permutations, and of course it’s at night because that’s when you use your lights.
So I just moved back to New York from a few years up in Cambridge Massachusetts, and around the time I first moved to Cambridge some new consumer EEG devices were coming on the market, or at least being brought to my attention.
And so with all these bike helmet projects you know in my pocket, I started to become interested in using EEG basically just as a hand free switch originally. So I had bike turn signals on my helmet and I became interested in you know what if I could use my brain to turn on my turn signals.
But it didn’t really work out that way. It can be quite challenging for any of you who have use an EEG device to use the device as an active controller. And in the process of working on this project called Mindrider, it became more interesting to see the EEG data that was coming out as a result of the form factor of the helmet and using the helmet on your bike.
So originally Mindrider was a very self-contained gadget. All it really contained was the EEG device, some processing and some micro-controller and some lights that visualized your state in real-time. But there was no real storage element, there was no real-time tracking element.
So this changed when a colleague of mine named Sandra Rickter who is here, asked me to modify some Mindriders for a study that she was doing on social cycling. So she does a lot of cycling as well but she’s much more interested in how social persuasions can bring new audiences into cycling.
So for this she was looking at for just women, just female novice cyclists and she wanted to see if there was any difference in EEG reading between a woman cycling alone or a woman cycling with a partner. So she had cyclist (A) ride alone and then she would meet up with cyclist (B) and then they would ride together. And then cyclist (B) would ride alone. She would take galvanic skin response, EEG, and interview data after every ride.
So that was a big change for Mindrider for me in that it turned the device intro a quantified Self device basically in that we were tracking EEG data of cyclists not just over time but also over space which I’ve come to see as one of the most interesting aspects of this project, so there’s kind of a multi-dimensionality to this project now.
This is a map from a cyclist that was wearing Mindrider, one of the prototypes every day in October during her commute. It was quite interesting to see patterns of where she experienced the most intense levels of concentration and then also the most intense levels of relaxation.
So I’ll explain the color codes to you a little bit. There’s a gradient from green to red. Green indicates high levels of meditation and relaxation, with low levels of attention and concentration. And then red is at the other end; high attention but low meditation. And so all the other colors are somewhere in between, and the brightness of the color indicates some of those two values.
So you can see she works around here in the meatpacking district and we could see every day as she approached work, her ride data would get more red, and every day as she rode over the Manhattan Bridge in which there is very little traffic between cyclists and pedestrians. And motorists we’d see that she pretty regularly her ride would become green over the course of that month.
So that was quite interesting to see those patterns and also start to think about the story that the insights that she could get out of these kinds of rides, you know, so this is my friend Tanya, who rode the Mindrider every day in the fall.
So not only knowing where heavy traffic but also approaching work, and knowing that these are seeing things visualize better are already pretty intuitive. She already knows that traffic you know can be stressful and that it makes her increase her concentration. She knows that getting to work can be stressful in that way as well, but she was very interested to see that over time and also to see the unexpected areas that perhaps were where she had high levels of concentration, and also to see unexpected areas where she had levels of relaxation and meditation.
So Tanya is an advanced cyclist. She rides everywhere she also rides on the weekends, and talking to her about about Mindrider as opposed to some of the novice cycling and the social cycling study, it was really interesting because Tanya tends to be more interested in where she can find more relaxation in her rides. She’s already using her ride to commute; you know she’s already using her ride to get exercise. And as a very busy person she’s interested in finding and using the ride as meditative practice, especially because she doesn’t like yoga or meditation.
So I thought that was quite interesting as opposed to cyclists not female’s per se, but not a cyclist from the social cycling study and other novice cyclists who I’ve spoken with who are very interested in knowing where they should be more cautious, you know as they’re planning their ride and getting used to just being a cyclist.
So something that I’m thinking a lot about now and working on this project is how all of our Quantified Self data can feed into a larger system that informs for instance, transportation planners, urban planners etc.
And the content of the class that I’m teaching focusses on quantified Self and the intersection of Quantified Self and location, I’m really interested in geolocation. It’s been fascinating to see how many of my students are also interested in location so that expands the notion even more. And how you know, that aggregation of data that then can become a dataset is at a level of an environmental dataset, you know, just in terms of it’s something that is very large scale, something over a very large time period or a large special area.
And so one of my goals in this project is for Mindrider to be helpful as a percussive device, so for instance if Tanya is trying to have a more relaxing ride, you know the helmet can potentially take a look at her back data and other kinds of data sources for instance her friends data, especially if she’s in a new place where she’s never ridden, or you know weather data etc. and then suggest for her that if she goes North she’ll have a more relaxing ride as opposed to if she goes South to get to the same destination. So this is something that I’m working on right now and thinking a lot about in terms of applying the lessons that I’m learning.
No that note, thanks so much.