Lifelike visual feedback for brain-computer interface

脑机接口（bci）将基本的心理指令转换为计算机介导的行动。脑机接口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。开发这些系统是为了帮助有运动缺陷的用户，这些缺陷可能源于：神经退行性疾病（如Lou Gehrig's病，或ALS），损伤（如脊髓损伤），甚至是环境限制，使运动困难或不可能（如穿着太空服的宇航员）。脑机接口系统通常需要大量的用户培训，以产生可重复的和独特的脑电波。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的心理意象形式来左右移动光标或条，例如移动右手与左脚。现实的视觉反馈解释运动动作应该大大提高脑机接口系统的可用性和性能。这一假设基于四个观察结果：1)人类已经进化到适应视觉和本体感觉反馈的运动控制；2)虚拟现实实验证明了运动的快速适应；3)对解码后的神经活动给予视觉反馈，动物的神经信号得到改善；4)解读动作的视觉反馈应激活镜像神经元系统，产生更强的动作信号。提出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图像的脑机接口系统。这项任务可以分为3个主要目标：1)在离线环境中使用视觉反馈分析运动图像；2)开发实时脑电分析算法；3)利用逼真的运动动画作为视觉反馈，构建实时脑机接口系统。虽然目标1和目标2的结果应该各自为BCI系统的当前技术状态做出贡献，但最大的BCI性能和可用性收益应该通过在第三个目标中将逼真的反馈引入在线范式来实现。该系统还可以通过研究正常受试者对系统的适应性来研究他们的学习和感觉运动加工。它还可以帮助确定植入物的最佳位置，从而为更昂贵的侵入性记录实验提供信息。所有为EEG信号处理和分析编写的软件都将作为EEGLAB的附加组件提供，EEGLAB是根据加州大学的研究、教育和非营利目的政策分发的。EEGLAB项目还在与圣地亚哥超级计算机中心合作开发EEG数据库。根据加州大学的政策，将通过该数据库发布具有代表性的数据集。

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.