EAGER: Brain-Mobile Interfaces: Exploratory Research into the Development of Networked NeuroPhones

由EAGER奖支持的研究正在开发NeuroPhone系统，这是第一个大脑-移动电话接口（BMI），它使人们在日常生活中佩戴的消费级无线脑电图（EEG）耳机的神经信号能够与移动电话接口，并与手机上现有的传感器流（例如，加速度计，陀螺仪，GPS）相结合，从而实现新形式的交互，通信和人类行为建模。具体来说，这项高风险的探索性研究是：1)研究低成本无线脑电图耳机和手机的新型节能技术和算法，用于使用消费设备对神经信号进行鲁棒感知、处理和占空比；2)为手机开发新的学习和分类算法，在嘈杂的移动环境中从脑电图耳机中提取和推断认知信息信号（如P300、N400和神经同步）；3)部署网络神经电话系统，重点关注实时多方神经同步以及网络神经电话之间神经信号的联网、隐私和共享；4)评估网络神经电话的应用，具体来说，测量师生在课堂上的参与度和测量群体层面的情绪状态。这项跨学科的研究在教育、教学和推广方面开辟了机会，部分原因是它专注于一个教育神经电话应用程序，它为学生在课堂上的认知参与提供了新的见解，并吸引了来自计算机科学系和心理与脑科学系的学生参与该项目。这项工作的结果将改变教育、健康监测和社交网络等不同领域的应用程序。

Research supported by this EAGER award 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; and4) 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.