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

HCC：小：迈向更自然和交互式的脑机接口

• 批准号: 1219200
• 负责人: Virginia de Sa
• 金额: $ 42.24万
• 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: 2012
• 资助国家: 美国
• 起止时间: 2012-09-01 至 2016-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1219200&HistoricalAwards=false
• 关键词: HCC; Small; Towards; more; natural

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.

脑机接口（BCI）将基本的心理命令翻译成计算机介导的动作。 BCI允许用户绕过外围运动系统，并通过大脑活动直接与世界互动。 这些系统正在开发，以帮助用户与运动缺陷源于神经退行性疾病，损伤，甚至环境限制，使运动困难或不可能。 一种流行的EEG驱动的BCI系统是基于想象的运动。 在这些系统中，用户通过运动想象与计算机交互，例如手与舌头运动的想象。 但是用户控制这种BCI的能力是非常可变的，并且所涉及的所有因素都没有完全理解。 例如，从离线训练到在线使用，EEG信号可能会发生巨大变化。 不幸的是，EEG的漂移可能导致BCI失去控制，这会导致用户沮丧，并且EEG信号从其训练基线进一步漂移。PI在该项目中的目标是通过专门解决失去控制和系统漂移来创建更强大的BCI系统。 她的假设是，明确地训练一个信号，结合用户的满意度，更重要的是，对当前性能的不满可能会导致一个更自然的界面，从而导致减少失控和改善系统的可用性和性能。 研究将分三个阶段进行。 首先，将在模拟在线设置中分析不满意和满意的主动和被动EEG信号。 接下来，将构建一个实时在线系统，该系统识别不满与满意以控制1-D光标移动，并将系统性能与标准的左/右运动想象系统进行比较。 最后，不满意/满意系统中最好的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。 的（不）满意度信号将基于主动控制的运动图像信号，解释的情绪，和检测错误的信号。更广泛的影响：该项目有可能大大提高基于EEG的BCI系统的鲁棒性，通过响应满意和不满的自然信号，通过抵抗漂移，并自然地利用挫折，这是一个常见的原因失控。 通过训练BCI识别挫折，PI期望将这种典型的负面特征转变为积极的。 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时间动态学习中心的合作伙伴关系，为代表性不足的REU参与者和高中生创建项目。（TDLC，她是REU计划的教师管理和招生委员会的成员）和Preuss学校（一所为没有受过大学教育的父母的低收入学生开设的特许学校）。 为EEG信号处理和分析编写的所有软件以及实验数据将作为EEGLAB的附加组件提供，EEGLAB由共同PI Makeig分发。

项目成果

Multi-Subject Unsupervised Transfer with Weighted Subspace Alignment for Common Spatial Patterns

• DOI: 10.1109/bci53720.2022.9735012
• 发表时间: 2022-02
• 期刊: 2022 10th International Winter Conference on Brain-Computer Interface (BCI)
• 作者: Zhining Chen;Mahta Mousavi;V. D. Sa

Hybrid brain-computer interface with motor imagery and error-related brain activity

具有运动想象和错误相关大脑活动的混合脑机接口

• DOI: 10.1088/1741-2552/abaa9d
• 发表时间: 2020
• 期刊: Journal of Neural Engineering
• 影响因子: 4
• 作者: Mousavi, Mahta;Krol, Laurens R.;de Sa, Virginia R.
• 通讯作者: de Sa, Virginia R.

Temporally Adaptive Common Spatial Patterns with Deep Convolutional Neural Networks

• DOI: 10.1109/embc.2019.8857423
• 发表时间: 2019-07
• 期刊: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)
• 作者: Mahta Mousavi;V. D. Sa