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

脑机接口(BCI)允许用户通过大脑活动直接与世界互动。开发这些系统是为了为患有严重运动障碍的用户提供一种通信方法，这些用户无法很好地控制手臂、舌头甚至眼睛的运动，无法以常规方式进行交流。虽然这些人的认知能力被认为在很大程度上得到了保护，但他们经常被描述为被“锁在”自己的身体里，无法通过打字、说话等常见方式与外部世界互动。基于脑电(EEG)的运动图像BCI试图通过测量用户想象移动身体不同部位引起的头皮上的电活动来区分大脑活动。通常，这样的系统试图区分用户何时在想象移动他们的右手或左手。想象不同的身体部位然后可以被映射到不同的任务，以允许用户与世界交互(例如，打开或关闭灯，或者将机器人手臂移动到一个或另一个对象)。这项研究的目标是通过改善给予用户的反馈和改善大脑信号的分类，使这些类型的系统更容易学习和更可靠。这项工作有可能为更多的人打开这种交流方法，项目成果可能会产生更广泛的影响，使我们能够更多地了解大脑信号，这些信号可以用于BCI中的交流。此外，不同的研究生将接受跨学科研究的培训，BCI班级的本科生将从事相关的小项目，其中一些将展示给高中生，以鼓励和激发他们对科学的兴趣。用户产生可区分的控制信号的能力非常不同。此外，其他大脑过程、情绪和疲劳等环境影响也会影响当前的脑机接口系统。本项目的目标是提高基于EEG的运动图像脑机接口的可用性。为此，将采取多管齐下的方法。首先，更丰富的反馈将让用户更好地可视化他们的图像效果，并为他们提供更好的机会来学习如何区分身体不同部位的运动图像。其次，将改进运动成像过程中脑电信号的机器分类。这将包括寻找其他信号，这些信号可能提供对用户顶层状态和目标的额外洞察，以及开发新的深度学习算法，这些算法可以受益于多任务学习和个人之间的迁移学习。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，并减少所需的训练试验次数。该小组先前的工作表明，对系统提供的反馈作出反应的交互信号对系统估计误差和非平稳性更稳健。这些信号可以被动地出现，但也可以通过利用随接收到的反馈而变化的交互命令来主动使用。无论是对交互命令的主动控制，还是对具有被动交互识别的标准命令的主动控制，都将得到更好的测试。这一奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

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.