Lifelike visual feedback for brain-computer interface

De Sa0756828大脑计算机接口(BCI)将基本的心理命令转换为计算机中介的操作。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。开发这些系统是为了帮助患有运动缺陷的用户，这些缺陷可能源于：神经退行性疾病(如卢·格里克病，或ALS)、损伤(如脊髓损伤)，甚至使行动困难或不能移动的环境限制(如穿太空服的宇航员)。BCI系统通常需要广泛的用户培训才能产生可重现的、清晰的脑电波。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的心理意象来左右移动光标或条，例如移动右手和左脚。对解释的运动动作进行真实的视觉反馈应该会大大提高BCI系统的可用性和性能。这一假说基于四个观察：1)人类已经进化到对视觉和本体感觉反馈做出反应，以适应他们的运动控制；2)在虚拟现实实验中证明了快速的运动适应；3)动物在给予其解码的神经活动的视觉反馈时，改善了他们的神经信号；以及4)解释的运动的视觉反馈应该激活镜像神经系统，产生更强的运动信号。这项拟议的工作旨在通过提供更自然、更逼真的反馈来改进现有的基于运动图像的脑机接口系统。这项任务可以分解为三个主要目标：1)在离线环境下分析具有视觉反馈的运动图像；2)开发实时脑电分析算法；3)构建一个以逼真的运动动画作为视觉反馈的实时脑-机接口系统。虽然目标1和目标2的结果本身应有助于BCI系统的当前技术水平，但应通过将逼真的反馈引入第三个目标的在线范例来实现最大的BCI性能和可用性收益。该系统还可以通过研究正常受试者对该系统的适应性来研究他们的学习和感觉-运动加工。它还可以通过帮助确定植入物的最佳位置来进行成本更高的侵入性记录实验。所有为脑电信号处理和分析编写的软件将作为EEGLAB的附加软件提供，该软件根据加州大学的政策分发，用于研究、教育和非营利目的。EEGLAB项目还与圣地亚哥超级计算机中心联合开发了一个脑电数据库。根据加州大学的政策，具有代表性的数据集将通过该数据库发布。

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.