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

脑机接口(BCI)将基本的心理命令翻译成计算机中介的动作。BCI允许用户绕过外围运动系统，通过大脑活动直接与世界互动。这些系统的开发是为了帮助患有运动障碍的用户，这些障碍源于神经退行性疾病、损伤，甚至是使行动困难或不能行动的环境限制。脑电驱动的脑-机接口系统的一种流行类型是基于想象的运动。在这些系统中，用户通过运动图像与计算机交互，例如想象手与舌头的运动。但用户控制这种BCI的能力非常不稳定，涉及的所有因素也没有完全了解。例如，从离线训练到在线使用，脑电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的失控，从而导致用户的挫折感和脑电信号进一步偏离训练基准线。她的假设是，在包含用户满意以及更重要的是对当前性能的不满的信号上进行显式训练可能会导致更自然的界面，从而减少失去控制并改善系统可用性和性能。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信号。接下来，将构建一个实时在线系统，该系统识别控制一维光标移动的不满与满意，并将系统性能与标准左/右运动成像系统的性能进行比较。最后，不满意/满意系统的最佳工作部分将与更标准的左/右系统相结合，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图像信号、解释的情绪和错误样信号的检测。广泛影响：该项目通过对满意和不满意的自然信号做出反应，通过抵抗漂移，以及通过自然利用沮丧这一常见的失控原因，有可能极大地提高基于脑电的脑-机接口系统的健壮性。通过训练BCI识别挫折，PI希望将这种典型的负面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不足的少数族裔研究生和博士后，并将通过国际学生联合会与NSF时间动态学习中心(TDLC，她是该中心教师管理和招生委员会的成员)和普赖斯学校(一所为没有受过大学教育的低收入学生设立的特许学校)的合作伙伴关系，为代表不足的REU参与者以及高中生创建项目。所有为脑电信号处理和分析编写的软件，以及来自实验的数据，都将作为EEGLAB的附加模块提供，该软件由co-Pi Makeig分发。

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.