Lifelike visual feedback for brain-computer interface

de Sa0756828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，直接通过大脑活动与世界互动。这些系统正在开发中，以帮助运动缺陷的用户，这些运动缺陷可能源于：神经退行性疾病（如Lou Gehrig病或ALS），损伤（如脊髓损伤），甚至使运动困难或不可能的环境限制（如宇航员在太空服中）。BCI系统通常需要广泛的用户培训，以生成可再现的和独特的脑电波。此外，直到最近，大多数BCI系统都以不直观或不自然的方式与用户交互，例如通过参与两种不相关形式的心理意象（例如移动右手与左脚）来左右移动光标或栏。解释运动动作的真实视觉反馈应该大大提高BCI系统的可用性和性能。这一假说基于四个观察：1）人类已经进化到适应他们的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适应; 3）动物在给予其解码的神经活动的视觉反馈时改善其神经信号;（4）解读运动的视觉反馈会激活镜像神经元系统，产生更强的运动信号。建议的工作旨在通过提供更自然和逼真的反馈来改进当前基于运动想象的BCI系统。这项任务可以分为3个主要目标：1）在离线环境中分析具有视觉反馈的运动想象; 2）开发用于实时EEG分析的算法;以及3）利用逼真的运动动画作为视觉反馈构建实时BCI系统。虽然目标1和2的结果应该各自对BCI系统的当前技术水平做出贡献，但最大的BCI性能和可用性增益应该通过将逼真的反馈引入第三个目标中的在线范例来实现。该系统也可以用来研究正常人的学习和感觉运动处理，通过研究他们的适应系统。它还可以通过帮助确定植入物的最佳位置来告知更昂贵的侵入性记录实验。 为EEG信号处理和分析编写的所有软件将作为EEGLAB的附加组件提供，EEGLAB根据加州大学的政策分发，用于研究、教育和非营利目的。EEGLAB项目还在与圣地亚哥超级计算机中心合作开发一个脑电图数据库。代表性数据集将根据加州大学的政策通过该数据库发布。

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.