HCC: Medium: Removing Barriers to the Practical Use of Non-Invasive Brain-Computer Interfaces

HCC：中：消除非侵入性脑机接口实际使用的障碍

• 批准号: 1065513
• 负责人: Charles Anderson
• 金额: $ 119.83万
• 依托单位: Colorado State University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2011
• 资助国家: 美国
• 起止时间: 2011-08-01 至 2018-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1065513&HistoricalAwards=false
• 关键词: HCC; Medium; Removing; Barriers; Practical

Brain-computer interfaces (BCIs) are hardware and software systems that allow users to interact with computer applications by changing their mental activity, which causes variations in weak electrical voltages produced by the brain. BCIs measure these voltages in one of two ways: invasive methods use electrodes implanted in the brain, while noninvasive methods use electrodes resting on the scalp that are part of a cap worn by the user. A long-term goal of BCI research is a new mode of communication for subjects with diseases and injuries resulting in the loss of voluntary muscle control, such as amyotrophic lateral sclerosis (ALS), multiple sclerosis, high-level spinal cord injuries or severe cerebral palsy. If all voluntary muscle control is lost, a locked-in syndrome results in which a person is unable to communicate with the outside world. BCIs can provide a new way for users to communicate with their caregivers and to control devices such as televisions, wheelchairs, speech synthesizers and computers. While BCI technology holds great promise, most BCI systems remain in research labs. The goal of this project is to remove barriers to practical, noninvasive, BCI technology that exist in current approaches, and to field test the resulting BCI systems in the homes of users who suffer from motor impairments. Limitations of current BCI systems that will be addressed include the difficulty of applying an electrode cap, signal artifacts due to other assistive technology in the user's environment, and long computer and user training times required to calibrate current EEG classification algorithms.A key barrier to practical BCI systems is the lack of methods for reliable, fast classification of EEG signals. In this project, this limitation will be addressed by conducting experiments in three areas. One set of experiments will investigate the quality of EEG signals recorded in subjects' homes and the performance of BCI applications in real-time in the homes. The second set of experiments will involve new algorithms for EEG artifact removal and signal classification that are tailored for EEG recorded in subjects' homes and for real-time use. For the second set of experiments, new user interfaces will be studied and compared to currently available interfaces. For the third set of experiments, several different user interface designs for BCI applications will be developed and studied. The effectiveness of visual and auditory feedback provided to the user in real-time will be investigated. This interdisciplinary project involves a team of investigators and students from diverse backgrounds. Faculty and students in computer science will design and implement algorithms and the BCI user interface. Faculty and students in occupational therapy will guide the field testing of BCI systems and will guide the evaluation of these experiments. Progress will be evaluated in a number of ways, including experiments comparing EEG signal representations and classifiers by accuracy, reliability, and training time, and field tests of BCI systems. Ultimately, the project's success will be measured by new or improved means of individuals interacting with computers in their homes for purposes of communication with others and control of assistive devices like wheelchairs.Broader Impacts: This project will develop a new technology for sensing and analyzing electroencephalogram signals (EEG) from human subjects. The resulting technology will help advance brain imaging and its application. The long term goal of this research is a new brain-computer interface based on EEG signals with which persons can use a computer to communicate with others in their vicinity or remotely over the net, to surf the net, and to control environmental entertainment, and assistive devices. The new technology will be simple enough for any person with minimal training to use. The project will also play a strong role in the education of future researchers and health professionals in this interdisciplinary field by involving graduate and undergraduate students from multiple departments as research assistants, by teaching a new course in BCI for students from a variety of backgrounds, and by providing fieldwork experiences.

脑机接口（bci）是一种硬件和软件系统，它允许用户通过改变他们的心理活动来与计算机应用程序进行交互，从而导致大脑产生的微弱电压的变化。脑机接口测量这些电压的方法有两种：侵入式方法使用植入大脑的电极，而非侵入式方法使用放置在头皮上的电极，这是用户戴的帽子的一部分。脑机接口研究的长期目标是为患有疾病和损伤导致随意肌控制丧失的受试者提供一种新的交流模式，如肌萎缩侧索硬化症（ALS）、多发性硬化症、高水平脊髓损伤或重度脑瘫。如果失去所有的随意肌控制，就会导致闭锁综合征，患者无法与外界交流。脑机接口可以为用户提供一种与护理人员沟通的新方式，并控制电视、轮椅、语音合成器和计算机等设备。虽然BCI技术具有很大的前景，但大多数BCI系统仍停留在研究实验室。该项目的目标是消除现有方法中存在的实用、非侵入性脑机接口技术的障碍，并在患有运动障碍的用户家中实地测试由此产生的脑机接口系统。将解决的当前BCI系统的局限性包括应用电极帽的困难，由于用户环境中其他辅助技术导致的信号伪影，以及校准当前EEG分类算法所需的长计算机和用户培训时间。实际脑机接口系统的一个关键障碍是缺乏可靠、快速的脑电信号分类方法。在这个项目中，这个限制将通过在三个领域进行实验来解决。其中一组实验将调查在受试者家中记录的脑电图信号的质量，以及BCI在家中实时应用的性能。第二组实验将涉及脑电图伪影去除和信号分类的新算法，这些算法是为在受试者家中记录的脑电图和实时使用而量身定制的。对于第二组实验，将研究新的用户界面，并将其与现有的界面进行比较。对于第三组实验，将开发和研究几个不同的BCI应用程序的用户界面设计。将调查实时提供给用户的视觉和听觉反馈的有效性。这个跨学科的项目涉及一个由来自不同背景的研究人员和学生组成的团队。计算机科学的教师和学生将设计和实现算法和BCI用户界面。职业治疗的教师和学生将指导脑机接口系统的现场测试，并指导这些实验的评估。将以多种方式评估进展，包括通过准确性、可靠性和训练时间比较脑电图信号表示和分类器的实验，以及脑机接口系统的现场测试。最终，这个项目的成功与否将由新的或改进的个人与家中电脑交互的方式来衡量，目的是与他人交流，并控制轮椅等辅助设备。更广泛的影响：该项目将开发一种新的技术，用于感知和分析来自人类受试者的脑电图信号。由此产生的技术将有助于推进脑成像及其应用。这项研究的长期目标是基于脑电图信号的一种新的脑机接口，人们可以使用计算机与附近或远程网络上的其他人交流，上网，控制环境娱乐和辅助设备。这项新技术将非常简单，任何受过最少训练的人都可以使用。该项目还将在这一跨学科领域的未来研究人员和卫生专业人员的教育中发挥重要作用，包括让来自多个系的研究生和本科生担任研究助理，为来自不同背景的学生教授脑机接口的新课程，并提供实地工作经验。