CHS: Small: Improving Usability and Reliability for Motor Imagery Brain Computer Interfaces

CHS：小型：提高运动想象脑机接口的可用性和可靠性

• 批准号: 1817226
• 负责人: Virginia de Sa
• 金额: $ 50万
• 依托单位: University of California-San Diego
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-09-01 至 2024-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1817226&HistoricalAwards=false
• 关键词: CHS; Small; Improving; Usability; Reliability; CHS; Small; Improving; Usability; Reliability

Brain-computer interfaces (BCIs) allow a user to interact with the world directly through brain activity. These systems are being developed to provide a communication method for users with severe motor impairments who are not able to control the movements of their arms, tongue, and even eyes well enough to communicate in the usual ways. While the cognitive abilities of these individuals are thought to be largely preserved, they are often described as being "locked in" to their bodies, unable to interact with the outside world through the usual means of typing, talking, etc. Electroencephalogram (EEG) based motor imagery BCIs attempt to distinguish brain activity by measuring electrical activity on the scalp caused by the user imagining moving different body parts. Commonly, such systems try to distinguish when the user is imagining moving their right or their left hand. Imagining different body parts can then be mapped to different tasks to allow a user to interact with the world (e.g., to turn a light on or off, or to move a robot arm to one object or another). The goal of this research is to make these types of systems easier for users to learn and more reliable, by improving the feedback that is given to the user and improving the classification of the brain signals. The work has the potential to open up this method of communication for more people, and project outcomes may have even broader impact by enabling us to learn more about brain signals that can be used for communication in BCIs. In addition, diverse graduate students will be trained in interdisciplinary research, and undergraduate students in the BCI class will work on small related projects, some of which will be presented to high school students to encourage and stimulate their interest in science.The ability of users to generate discriminable control signals is very variable. Moreover, environmental effects such as other brain processes, emotion and fatigue affect current BCI systems. The goal of this project is to improve the usability of EEG-based motor-imagery brain-computer interfaces. To this end, a multi-pronged approach will be used. First, richer feedback will give users a better visualization of the effects of their imagery and provide them with a better chance to learn how to discriminate the motor imagery of different body parts. Second, the machine classification of the EEG signal during motor imagery will be improved. This will include looking for other signals that may provide additional insight into the top-level state and goals of the user as well as developing new deep learning algorithms that can benefit from multi-task learning and transfer learning between individuals. Third, different closed-loop control methods will be explored to improve the total information transfer rate of the BCI as well as to reduce the number of training trials needed. The team's prior work has shown that interactive signals that respond to the feedback provided by the system are more robust to system estimation errors and non-stationarities. These signals can arise passively but also can be actively used by exploiting interactive commands that vary with the received feedback. Whether active control of interactive commands, or active control of standard commands with passive interactive recognition, performs better will be tested.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

脑部计算机界面（BCIS）允许用户通过大脑活动直接与世界互动。 正在开发这些系统，以为无法控制其手臂，舌头甚至眼睛的动作的严重运动障碍的用户提供通信方法，以便以通常的方式进行交流。 尽管认为这些人的认知能力在很大程度上被保存了，但通常被描述为“锁定”到他们的身体上，无法通过打字，说话等的通常手段与外界互动。基于电脑图（EEG）的电动图像BCIS BCIS试图通过在脑皮物上测量脑力活性来通过用户的体系进行测量来区分大脑活动。 通常，此类系统试图区分用户何时想象移动右手或左手。 然后可以将不同的身体部位映射到不同的任务，以使用户与世界互动（例如，打开或关闭灯，或将机器人臂移至一个对象或另一个对象）。 这项研究的目的是通过改善给用户的反馈并改善大脑信号的分类，使这些类型的系统更容易为用户学习和更可靠。 这项工作有可能为更多人开放这种交流方法，并且通过使我们能够更多地了解可用于BCIS通信的大脑信号的更多信息，可能会产生更大的影响。 此外，多元化的研究生将接受跨学科研究的培训，而BCI课程的本科生将从事小型相关项目，其中一些将呈现给高中生，以鼓励和刺激他们对科学的兴趣。此外，其他大脑过程，情绪和疲劳等环境影响会影响当前的BCI系统。 该项目的目的是提高基于脑电图的电动机象征脑机构接口的可用性。为此，将使用多管齐下的方法。 首先，更丰富的反馈将使用户更好地可视化图像的影响，并为他们提供更好的机会学习如何区分不同身体部位的运动图像。 其次，将改善运动图像期间脑电图信号的机器分类。 这将包括寻找其他信号，这些信号可能会提供有关用户顶级状态和目标的更多见解，以及开发新的深度学习算法，这些算法可以受益于个人之间的多任务学习和转移学习。 第三，将探索不同的闭环控制方法，以提高BCI的总信息传输率，并减少所需的培训试验数量。 该团队的先前工作表明，对系统提供的反馈做出响应的交互信号对系统估计错误和非平稳性更为强大。 这些信号可以被动地出现，但也可以通过利用随着收到的反馈而变化的交互式命令来积极使用。 无论是积极控制交互式命令还是通过被动互动识别的积极控制标准命令，都将得到更好的测试。该奖项反映了NSF的法定任务，并且使用基金会的知识分子优点和更广泛的影响评估审查标准，被认为值得通过评估来获得支持。