We were fascinated about the wide range of things neurofeedback could accomplish (treat ADHD, Anxiety, Stress) and we would like to make that futuristic technology cheap and accessible for general public. With the advent of wearable technology for analyzing Brainwaves like Neurosky, Emotiv, and Muse, we thought we can make this thing happen. Our hack is the beginning of a new division in brain workouts.

How it works

We employ two kinds of feedback here, vibratory feedback and visual feedback. We used pebble smartwatch to provide vibratory feedback and we employed smartthings to get the visual feedback. This hack is for students who would want to improve their productivity. Vibratory feedback: When a student is concentrating on his/her studies we use Neurosky Mindwave to to detect his/her concentration levels and we vibrate Pebble watch when he/she gets distracted. Visual feedback: When a student wants to relax for a while, he can just try relaxing and when he/she reaches a particular state of relaxation, the light turns off, and the challenge is to keep the light off.

Challenges we ran into

The MindWave does very little to abstract away the cryptic EEG intensity data the device reads out. Our team was tasked with taking the raw data readings regarding the intensity of various brain wave frequencies and mapping them to more abstract concepts regarding relaxation, attention, and drowsiness. Extensive experimentation, signal processing, and data analysis techniques were explored and employed through our project. Our team also needed to explore various devices and APIs across multiple platforms in order to connect our MindWave device to the world. This included connecting Pebble, Smartthings, and MindWave technologies through Android, Mac, and Web platforms.

Accomplishments that I'm proud of

In our short time, our team explored many new technologies with different underlying tools, designs, and challenges, yet we continuously managed to adapt to the demands of our project. Even with our varied backgrounds and skills, everyone still put in the work to read documentation, develop, test, debug, and read even more documentation. Especially on the data analysis end, our team dug deep and far for ways to better our understanding of brainwaves and their relation to our physiology, mentality, and lifestyle. Our results are not only a product of our hard work but our passion and understanding for the prospects and potential of neurofeedback.

What I learned

What's next for Phocus

Our team is continuing to explore machine learning and data mining techniques in order to better extrapolate the physiological state of the user. We have experimented with Azure's machine learning cloud solutions and hope to fully utilize their technologies to streamline and execute the model training process. Our team is also exploring other technologies which could be leveraged in the home or student's lives to better productivity, including temperature control based on drowsiness, etc

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