HCC: Small: Towards more natural and interactive brain-computer interfaces

脑机接口（bci）将基本的心理指令转换为计算机介导的行动。脑机接口允许用户绕过外围运动系统，直接通过大脑活动与世界互动。这些系统的开发是为了帮助因神经退行性疾病、损伤或环境限制而导致运动困难或不可能的运动缺陷的用户。一种流行的脑电图驱动的脑机接口系统是基于想象的运动。在这些系统中，用户通过想象手和舌头的运动来与计算机交互。但是，用户控制这种脑机接口的能力是非常多变的，而且所涉及的所有因素还没有完全了解。例如，从离线训练到在线使用，脑电图信号可能会发生巨大变化。不幸的是，脑电信号的漂移会导致脑机接口失去控制，从而导致用户受挫，并进一步偏离训练基线。PI在这个项目中的目标是通过专门解决控制丢失和系统漂移问题来创建一个更健壮的BCI系统。她的假设是，明确地对包含用户满意度的信号进行训练，更重要的是，对当前性能的不满可能会导致更自然的界面，从而减少控制损失，提高系统可用性和性能。研究将分三个阶段进行。首先，在模拟的在线环境中，分析不满意和满意的主动和被动脑电图信号。接下来，将构建一个实时在线系统，识别控制1-D光标移动的不满和满意，并将系统性能与标准左/右运动图像系统进行比较。最后，不满意/满意系统的最佳工作部分将与更标准的左右系统集成，以创建更好的混合系统。（不）满意信号将基于主动控制的运动意象信号、解释的情绪和错误信号的检测。更广泛的影响：通过响应满意和不满意的自然信号，通过抵抗漂移，通过自然地利用挫败感（这是失去控制的常见原因），该项目有可能极大地提高基于脑电图的BCI系统的稳健性。通过训练BCI识别挫折感，PI期望将这种典型的消极特征转变为积极特征。该项目将在这一重要的跨学科领域支持和培训一名代表性不足的少数民族研究生和一名博士后，并将通过PI与NSF学习时间动态中心（TDLC）的合作，为代表性不足的REU参与者以及高中生创建项目。在那里，她是REU项目的教师管理和招生委员会的成员)和Preuss学校（一所面向没有受过大学教育的父母的低收入学生的特许学校）。所有为EEG信号处理和分析编写的软件，以及实验数据，将作为附加组件提供给由co-PI Makeig分发的EEGLAB。

Brain computer interfaces (BCIs) translate basic mental commands into computer-mediated actions. BCIs allow the user to bypass the peripheral motor system and to interact with the world directly via brain activity. These systems are being developed to aid users with motor deficits stemming from neurodegenerative disease, injury, or even environmental restrictions which make movement difficult or impossible. One popular class of EEG-driven BCI systems is based on imagined movement. In these systems the user interacts with a computer through motor imagery such as the imagination of hand vs. tongue movement. But the ability of users to control such a BCI is very variable, and all the factors involved are not fully understood. For example, EEG signals can change drastically from offline training to online use. Unfortunately, drift in EEG can lead to loss of control of the BCI, which leads to user frustration and further drift of EEG signals from their training baselines.The PI's goal in this project is to create a more robust BCI system by specifically addressing loss of control and system drift. Her hypothesis is that explicitly training on a signal that incorporates a user's satisfaction and, more importantly, dissatisfaction with the current performance may result in a more natural interface, and thereby lead to a reduction in loss of control and improved system usability and performance. The research will be carried out in three stages. First, active and passive EEG signals of dissatisfaction and satisfaction will be analyzed in a simulated online setting. Next, a real-time online system that recognizes dissatisfaction vs. satisfaction to control 1-D cursor movement will be constructed and system performance compared to that of a standard left/right motor imagery system. Finally, the best working parts of the dissatisfaction/satisfaction system will be integrated with the more standard left/right system, to create a better hybrid system. The (dis)satisfaction signals will be based on actively controlled motor imagery signals, interpreted emotion, and detection of error-like signals.Broader Impacts: This project has the potential to vastly improve the robustness of EEG-based BCI systems, by responding to natural signals of satisfaction and dissatisfaction, by being resistant to drift, and by naturally taking advantage of frustration which is a common cause of loss of control. By training the BCI to recognize frustration the PI expects to turn this typically negative trait into a positive. The project will support and train an under-represented minority graduate student and a post-doc in this important interdisciplinary area, and it will create projects for under-represented REU participants as well as for high school students through the PI's partnerships with the NSF Temporal Dynamics of Learning Center (TDLC, where she is a member of the faculty governing and admissions committee for the REU program) and the Preuss School (a charter school for low income students with no college educated parent). All software written for EEG signal processing and analysis, as well as data from the experiments, will be made available as add-ons to EEGLAB which is distributed by co-PI Makeig.