NYC is now considered one of the best cities for biking in the U.S., but resident cyclists know that navigating its streets can still be a stressful experience. MIT Media Lab’s Arlene Ducao is hoping to shed some light on which biking routes promise a more relaxing ride with her Mindreader Map. The project is a continuation of Ducao’s 2012 experiments involving a mind-controlled bicycle helmet that flashes different colors depending on the stress levels of the rider.
To create the Mindreader Map, Ducao recruited eight riders to spend the months of September and October traversing Manhattan’s streets while wearing Mindreader helmets. Every second, an off-the-shelf electroencephalogram (EEG) reader fitted inside the helmet would register the cyclist’s level of focus.
In total, the cyclists’ activities accumulated 80,000 data points relating to their attention levels at different points around the city. These points were ranked from 0 to 100, color-coded from green to yellow to red and then plotted on corresponding points on the Mindreader Map.
If a cyclist were to be dealing with hazards in the road, then their attention level would be greater and that moment would be plotted in red. If the cyclist were cruising happily along a pleasant section of Riverside Drive, however, they would be more relaxed, and their attention level would be lower and plotted in green.
The potentials for this kind of mapping are wide-ranging and really quite intriguing. Using this type of data city planners could conceivably better plan bike lanes and traffic signals with an understanding of where the most stressful, and potentially dangerous, areas for cyclists are.
But first Ducao’s technique would need a little fine-tuning; as she admits, this particular iteration of the Mindreader Map “arose from our curiosity,” and the data is not without its flaws. Only one cyclist biked down each street, and one individual may be more or less attentive than another, or they may have been distracted by non-cycling related factors. Furthermore, traffic may have been worse at one particular time than another.
The equipment used could also use some fine-tuning. As Wired explains, “off-the-shelf EEG readers don’t provide the same level of finely tuned data obtained by research-level gear,” and the way in which the readers are connected to the skin and used also must be consistent for any data to be accurate.
So there’s certainly more work to be done and more data to be collected before a map of this kind can be genuinely useful, but in the future this could be an exceptionally handy tool for anyone from urban planners designing better traffic flow to tourists looking for a leisurely afternoon ride.