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）是硬件和软件系统，允许用户通过改变他们的心理活动与计算机应用程序进行交互，这会导致大脑产生的弱电压的变化。 脑机接口以两种方式之一测量这些电压：侵入性方法使用植入大脑中的电极，而非侵入性方法使用放置在头皮上的电极，这些电极是用户戴的帽子的一部分。 BCI研究的一个长期目标是为患有导致自主肌肉控制丧失的疾病和损伤的受试者提供一种新的交流模式，如肌萎缩性侧索硬化症（ALS），多发性硬化症，高水平脊髓损伤或严重脑瘫。 如果失去了所有的自主肌肉控制，就会导致闭锁综合征，即一个人无法与外界交流。 BCI可以为用户提供一种新的方式来与他们的护理人员进行沟通，并控制电视，轮椅，语音合成器和计算机等设备。 尽管BCI技术前景广阔，但大多数BCI系统仍停留在研究实验室中。 该项目的目标是消除现有方法中存在的实用，非侵入性BCI技术的障碍，并在患有运动障碍的用户家中现场测试由此产生的BCI系统。 当前脑机接口系统的局限性包括难以应用电极帽、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分类算法所需的较长计算机和用户培训时间。实用脑机接口系统的一个关键障碍是缺乏可靠、快速的EEG信号分类方法。 在本项目中，将通过在三个领域进行实验来解决这一限制。 其中一组实验将调查在受试者家中记录的EEG信号的质量以及BCI应用程序在家中的实时性能。 第二组实验将涉及用于EEG伪影去除和信号分类的新算法，这些算法针对受试者家中记录的EEG和实时使用而定制。 对于第二组实验，将研究新的用户界面，并与当前可用的界面进行比较。 对于第三组实验，将开发和研究用于BCI应用的几种不同的用户界面设计。 将研究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调查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BCI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EEG信号表示和分类器的实验，以及BCI系统的现场测试。 最终，该项目的成功将通过个人与家中计算机进行交互的新的或改进的手段来衡量，以实现与他人交流和控制轮椅等辅助设备的目的。更广泛的影响：该项目将开发一种新技术，用于感测和分析来自人类受试者的脑电图信号（EEG）。 由此产生的技术将有助于推进大脑成像及其应用。这项研究的长期目标是一个新的脑机接口的基础上的EEG信号，人们可以使用计算机与他人在他们附近或远程通过网络进行通信，网上冲浪，并控制环境娱乐，和辅助设备。 这项新技术对于任何受过最少培训的人来说都很简单。 该项目还将在这个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，包括来自多个部门的研究生和本科生作为研究助理，为来自各种背景的学生教授BCI新课程，并提供实地工作经验。