HCC: Human-Computer Interaction and Brain Measurement Using fNIR Spectroscopy

HCC：使用 fNIR 光谱进行人机交互和大脑测量

• 批准号: 0713506
• 负责人: Robert Jacob
• 金额: $ 44.46万
• 依托单位: Tufts University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2007
• 资助国家: 美国
• 起止时间: 2007-08-01 至 2011-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0713506&HistoricalAwards=false
• 关键词: HCC; Human; Computer; Interaction; Brain

There has been much previous research in the general area of brain-computer interfaces (BCI), which has primarily aimed to help people with severe motor disabilities interact with their environment by translating their brain activity into specific device control signals. Users who are completely paralyzed ("locked-in") or those who lack muscle control (e.g., people with cerebral palsy or stroke victims) can use BCIs to answer simple questions, to control their environment, and for word processing. But the variety of brain imaging techniques that have been tried to date for BCIs have all suffered from serious drawbacks, so that communicating through such systems is currently time consuming and mentally demanding. Open research challenges concern the accuracy of BCIs (systems often misinterpret a user's intentions), and the information transfer rate of such systems (which is often too slow for use in real world settings). In this project, the PIs will build on their experience in designing, implementing, and evaluating non-command, adaptive user interfaces (e.g., based on eye movement), to advance BCI by bringing to bear emerging technology for measuring brain activity using functional near infrared (fNIR) spectroscopy coupled with machine learning to analyze user data in human-computer interaction. While fNIR technology is still in its infancy, the PIs expect its use as a real-time input to an adaptive interface to break new ground. The PIs believes that this combination will also lead to new, more objective methods for evaluating next generation interaction styles for HCI in general. The PIs' approach is a natural extension of and will exploit their prior work relating to the concept of reality-based interaction, which focuses on the ways that new interaction styles exploit the user's pre-existing skills and expectations from the real world more than trained computer skills, while at the same time helping to differentiate mental effort devoted to interface-related or syntactic aspects from that devoted to the underlying task or semantic aspects.Broader Impacts: This project will develop technologies of immediate benefit to users with motor disabilities. Additionally, the work will open up a new area of HCI research (namely, relating to interfaces that can dynamically adapt in real time to the user's cognitive load), and will advance the theory and evaluation of interaction styles in general.

之前在脑机接口（BCI）的一般领域已经进行了大量研究，其主要目的是通过将大脑活动转化为特定的设备控制信号来帮助患有严重运动障碍的人与环境互动。 完全瘫痪（“锁定”）的用户或缺乏肌肉控制的用户（例如，脑性麻痹或中风患者）可以使用脑机接口来回答简单的问题，控制他们的环境，以及进行文字处理。 但是，迄今为止用于脑机接口的各种脑成像技术都存在严重的缺陷，因此，通过这种系统进行交流目前既耗时又费神。 开放式研究的挑战涉及BCI的准确性（系统经常误解用户的意图），以及此类系统的信息传输速率（对于在真实的世界环境中使用而言，通常太慢）。 在这个项目中，PI将建立在他们在设计、实现和评估非命令、自适应用户界面（例如，基于眼球运动），通过使用功能性近红外（fNIR）光谱结合机器学习来分析人机交互中的用户数据来测量大脑活动的新兴技术来推进BCI。 虽然fNIR技术仍处于起步阶段，但PI希望将其用作自适应界面的实时输入，以开辟新的领域。 PI认为，这种组合也将导致新的，更客观的方法来评估下一代人机交互风格。 PI的方法是一个自然的延伸，并将利用他们以前的工作有关的概念，基于现实的互动，重点是新的互动风格的方式，利用用户的预先存在的技能和期望从真实的世界比训练有素的计算机技能，与此同时，帮助区分致力于界面的脑力劳动，更广泛的影响：本项目将开发对运动障碍用户立即有益的技术。 此外，这项工作将开辟一个新的领域的人机交互研究（即，有关的接口，可以动态地适应在真实的时间，以用户的认知负荷），并将推进理论和评估的互动风格一般。