The topic I would to research and explore throughout this semester is cognitive computing interfaces. I want to explore EEG sensors that are currently available in market. I will pair EEG data with GPS and other sensors such as light, sound, & pulse. There are three major challenges with this project. The first challenge is the physical computing aspect. I would need to get all the sensors to work together with a microprocessor and be able to log the data. The second challenge would be to visualize all these data together in a way that’s comprehensible and show their relevance to the user’s daily activities. The third challenge is to package the sensors and other electronics components into a visually pleasing and comfortable wearable device.
The only two reliable readings out of all the EEG readings are Attention and Meditation. Working within the limitation of this current available technology, I will only be relying on the Attention reading to use for logging and visualization.
It will be interesting to use this device as a tool to boost online education performances. There are many free online courses available but many people fail to follow through and complete the courses that they have signed up or started with. This device can potentially support the student working on an online course by alerting her when her attention level has dropped. This would allow her to quickly refocus on the lesson and shorten the length of time that’s needed to mentally process the video lesson.
As mentioned above, one of the main challenge of this project is visualizing all these biometric, GPS, and environmental data together in a way that’s useful, readable, and aesthetically pleasing. The wearable market is becoming hugely popular and is expanding rapidly. However, the pairing mobile applications and the data visualization begs major improvements. Take Fitbit for instance, the number and line graphs regarding my daily steps gives me a very superficial understanding of my daily activities. If more sensory data were gathered and presented in tandem with this step count maybe it will give me a better understanding of my day. However, the challenge with having more data is then how to show the relationship between of these datas, how they affect or complement each other, and most of all how can the user relate these data with their daily life.
I’m excited for this project because it ties in with my industrial design background and my current interests with wearable devices and data visualization. I hope to also create an Android phone app that would pair with EEG device to log and visualize the gathered data. As for the form of the device, I am picturing something similar to the Melon. The form would be simple band but somehow communicates its EEG capability more than just a band that wraps around the head. Instead of plastic and rubber, the device could possible be constructed with fabric or more flexible materials. This will provide form fitting comfort as well as a unique look that will set it apart from other EEG devices available in the market.
My intended user would be adults within the early twenties to late thirty age range. They would be urban dwellers who would most often travel by public transportations and walking. The GPS component would be use to log the location that produces the highest level of concentration. This location information as well as other data such as sound and light data will help the user to recognise their ideal study location, time, and environment. This understanding of the ideal studying environment pairing with the refocusing alert feature could potentially boost the user’s overall online educational experience and performance.
“Activity Recognition for the Mind: Toward a Cognitive “Quantified Self”” Activity Recognition for the Mind: Toward a Cognitive “Quantified Self” Web. 25 Mar. 2014. <http://www.m.cs.osakafu-u.ac.jp/publication_data/1362/mco2013100105.pdf>.
“NeuroPlace: Making Sense of a Place.” NeuroPlace. Web. 25 Mar. 2014. <http://dl.acm.org/citation.cfm?id=2459267>.
“Recognizing the Degree of Human Attention Using EEG Signals from Mobile Sensors.” Sensors. Web. 25 Mar. 2014. <http://www.mdpi.com/1424-8220/13/8/10273>.