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

在这一热切奖项的支持下，研究人员正在开发NeuroPhone系统，这是第一个脑-移动电话接口(BMI)，使人们在日常生活中佩戴的消费级无线脑电(EEG)耳机的神经信号能够连接到手机，并与电话上现有的传感器流(例如加速计、陀螺仪、GPS)相结合，实现新形式的交互、通信和人类行为建模。具体地说，这项高风险的探索性研究是为了：1)研究用于低成本无线EEG耳机和手机的新的节能技术和算法，用于使用消费设备对神经信号进行稳健的感知、处理和占空循环；2)为手机开发新的学习和分类算法，以在嘈杂的移动环境中从EEG耳机中提取和推断认知信息信号(例如，P300、N400和神经同步)；3)部署联网的神经电话系统，重点是实时多方神经同步以及联网的神经电话之间的神经信号的联网、隐私和共享；4)评估联网的脑机应用程序，特别是，测量师生在课堂上的参与度和测量小组水平的情绪状态。这项跨学科的研究在教育、教学和外展方面打开了机会，部分原因是它专注于教育脑机应用程序，它为学生在课堂上的认知投入提供了新的见解，并吸引了计算机科学系和心理科学系和脑科学系的学生参与该项目。这项工作的结果将改变不同领域的应用程序，如教育、健康监测和社交网络。

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.