Lifelike visual feedback for brain-computer interface

de Sa0756828脑机接口（BCI）将基本的心理命令转化为计算机介导的动作。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。这些系统的开发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（例如卢伽雷氏病或 ALS）、损伤（例如脊髓损伤），甚至是导致运动困难或不可能的环境限制（例如穿着宇航服的宇航员）。 BCI 系统通常需要大量的用户培训才能生成可重复且独特的脑电波。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来左右移动光标或条，例如移动右手与左脚。解释运动动作的真实视觉反馈应能显着提高 BCI 系统的可用性和性能。这一假设基于四个观察结果：1）人类已经进化到能够根据视觉和本体感觉反馈来调整其运动控制； 2）虚拟现实实验证明了快速运动适应； 3）当动物获得解码的神经活动的视觉反馈时，它们的神经信号会得到改善； 4）解释运动的视觉反馈应该激活镜像神经元系统，产生更强的运动信号。拟议的工作旨在通过提供更自然、更逼真的反馈来改进当前基于运动想象的脑机接口系统。该任务可以分为 3 个主要目标：1）在离线环境中通过视觉反馈分析运动想象； 2）开发实时脑电图分析算法； 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通过在第三个目标中的在线范例中引入逼真的反馈来实现。该系统还可以通过研究正常受试者对系统的适应来研究他们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位置来为成本更高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，并根据加州大学用于研究、教育和非营利目的的政策进行分发。 EEGLAB 项目还与圣地亚哥超级计算机中心合作开发 EEG 数据库。代表性数据集将根据加州大学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.