Lifelike visual feedback for brain-computer interface

脑机接口逼真的视觉反馈

• 批准号: 0756828
• 负责人: Virginia de Sa
• 金额: $ 27.54万
• 依托单位: University of California-San Diego
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2008
• 资助国家: 美国
• 起止时间: 2008-07-01 至 2011-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0756828&HistoricalAwards=false
• 关键词: Lifelike; visual; feedback; brain; computer

de Sa0756828Brain computer interfaces (BCIs) translate basic mental commands into computer-mediated actions. BCIs allow the user to bypass the peripheral motor system and interact with the world directly through brain activity. These systems are being developed to aid users with motor deficits which can stem from: neurodegenerative disease (such as Lou Gehrig's disease, or ALS), injury (such as spinal cord injury), or even environmental restrictions which make movement difficult or impossible (such as astronauts in space suits). BCI systems typically require extensive user training to generate reproducible and distinct brain waves. Furthermore, until very recently, most BCI systems have interacted with the user in unintuitive or unnatural ways, such as moving a cursor or bar left and right by engaging in two unrelated forms of mental imagery, such as moving the right hand vs. the left foot. Realistic visual feedback of interpreted motor action should substantially improve usability and performance of BCI systems. This hypothesis is based on four observations: 1) humans have evolved to adapt their motor control in response to visual and proprioceptive feedback; 2) rapid motor adaptation is demonstrated in virtual reality experiments; 3) animals improve their neural signal when given visual feedback of their decoded neural activity; and 4) visual feedback of interpreted movement should activate the mirror neuron system, producing a stronger movement signal. The proposed work aims to improve upon current BCI systems based on motor imagery by providing more natural and lifelike feedback. This task can be broken down into 3 main objectives: 1) analyze motor imagery with visual feedback in an offline setting; 2) develop algorithms for real-time EEG analysis; and 3) construct a real-time BCI system utilizing lifelike motion animations as visual feedback. While results of objectives 1 and 2 should each in their own right contribute to the current state of the art in BCI systems, the largest BCI performance and usability gains should be made by introducing lifelike feedback into an online paradigm in the third objective. The proposed system can also be used to study learning and sensory-motor processing in normal subjects by studying their adaptation to the system. It may also inform more costly invasive recording experiments by helping to determine optimal placements of implants. All software written for EEG signal processing and analysis will be made available as add-ons to EEGLAB which is distributed in accordance with University of California policy for research, education, and non-profit purposes. The EEGLAB project is also developing an EEG database in conjunction with the San Diego Supercomputer Center. Representative data sets will be released via this database in accordance with University of California policy.

DE SA0756828BRAIN计算机接口（BCIS）将基本的心理命令转化为计算机介导的动作。 BCIS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。这些系统的开发是为了帮助患有运动缺陷的用户，可能源于：神经退行性疾病（例如Lou Gehrig病或ALS），损伤（例如脊髓损伤），甚至是使运动变得困难或不可能的环境限制（例如太空套件中的Astronauts）。 BCI系统通常需要大量的用户培训来产生可重现和不同的脑电波。此外，直到最近，大多数BCI系统都以不直觉或不自然的方式与用户进行交互，例如通过参与两种无关的心理图像形式，例如移动右手与左脚，将光标或左右右侧移动。解释的运动动作的现实视觉反馈应大大改善BCI系统的可用性和性能。该假设基于四个观察：1）人类已经进化以适应其运动控制，以响应视觉和本体感受反馈； 2）在虚拟现实实验中证明了快速运动的适应性； 3）当鉴于其解码神经活动的视觉反馈时，动物会改善其神经信号； 4）解释运动的视觉反馈应激活镜像神经元系统，从而产生更强的运动信号。拟议的工作旨在通过提供更自然和栩栩如生的反馈来改善基于运动图像的当前BCI系统。该任务可以分为3个主要目标：1）在离线设置中以视觉反馈分析运动图像； 2）开发用于实时脑电图分析的算法； 3）构建一个实时的BCI系统，该系统利用栩栩如生的运动动画作为视觉反馈。虽然目标1和2的结果应自己权利，因此在BCI系统中的当前状态应有助于BCI的最大性能和可用性提高，应通过将寿命的反馈引入第三个目标中的在线范式中。所提出的系统还可以通过研究对系统的适应来研究正常受试者的学习和感觉运动处理。它还可以通过帮助确定植入物的最佳放置来为更昂贵的侵入性记录实验提供信息。 所有用于EEG信号处理和分析的软件将作为EEGLAB的附加组件提供，该软件根据加利福尼亚大学的研究，教育和非营利性目的分发。 EEGLAB项目还与圣地亚哥超级计算机中心共同开发了一个EEG数据库。代表性数据集将根据加利福尼亚大学政策通过此数据库发布。