NSF is funding a project for is developing the NeuroPhone system, the first Brain-Mobile phone Interface (BMI) that enables neural signals from consumer-level wireless electroencephalography (EEG) headsets worn by people as they go about their everyday lives to be interfaced to mobile phones and combined with existing sensor streams on the phone (e.g., accelerometers, gyroscopes, GPS) to enable new forms of interaction, communications and human behavior modeling.

Specifically, this high-risk exploratory research is to:

1) study new energy-efficient techniques and algorithms for low-cost wireless EEG headsets and mobile phones for robust sensing, processing and duty cycling of neural signals using consumer devices;

2) develop new learning and classifications algorithms for the mobile phone to extract and infer cognitively informative signals (e.g., P300, N400, and neural synchrony) from EEG headsets in noisy mobile environments;

3) deploy networked NeuroPhone systems with a focus on real-time multi-party neural synchrony and the networking, privacy and sharing of neural signals between networked NeuroPhones; and

4) evaluate networked NeuroPhones applications, specifically, measuring teacher-student engagement in the classroom and measuring group level emotional state.

This interdisciplinary research opens up opportunities in education, teaching and outreach, in part because it focuses on an educational NeuroPhone application, which contributes new insights into cognitive engagements of students in the classroom as well as engages students from the Department of Computer Science and the Department of Psychological and Brain Sciences in the project. Results from this work will transform applications across diverse domains such as education, health monitoring, and social networking.