CHS: Small: A Novel P300 Brain-Computer Interface

脑机接口（bci）将基本的心理命令转换为计算机介导的行动，从而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因神经退行性疾病、损伤或环境限制而导致运动困难或不可能的运动缺陷的用户。脑电图驱动的脑机接口系统中最成功的一类是P300，它通过检测用户对闪现刺激的反应来工作。在大多数P300系统中，呈现字母和/或其他符号的网格，并且符号的行或列以随机顺序闪烁；用户关注所需的符号（通常是在它闪烁时静静地计数）。这些基于网格的P300系统的一个主要问题是，用户必须理想地看着闪烁的目标，尽量少地注意那些微小的字母，但是晚期渐冻症和其他闭锁患者最需要这些系统，他们在注视目标和控制眼球运动方面存在困难。PI的假设是，闪现一个大字母片段的BCI可以保留同时查询多个字母的组合效率，同时具有一个中心焦点的优势（不需要转移视线）。本研究旨在设计和测试这种新的词法拼写器。项目成果有可能极大地提高基于P300脑电图的脑机接口系统的可用性，为那些有视觉、感觉和运动障碍的人服务。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由加州大学圣地亚哥分校的斯沃茨计算神经科学中心（SCCN）和学习时间动态中心的一部分分发。数据也将通过同样由SCCN管理的HeadIT数据档案提供。这项研究任务可以分为三个主要目标：开发和测试对闪现片段的反应；改进对闪现片段反应的单次分类；并设计一个逻辑来选择片段和解释他们的反应。开发的系统将为BCI拼写控制提供另一种方法，不依赖于转移视线的能力。PI认为，由于能够一次探测多个字母，这种方法将比其他空间不变BCI拼写器具有更高的信息传输速率。除了对那些眼球运动受损和/或视力受损的人有利外，该方法还应具有优于标准P300系统的其他优点。当出现错误时，它们往往是视觉上相似的符号。结合语言先验和主动片段选择很容易适应，这可能导致更高的信息传输速率与较慢的闪速。此外，提高对单次试验时间脑电图信号的识别和将贝叶斯语言模型纳入拼写器的工作可能对其他类型的脑机接口有用。

Brain computer interfaces (BCIs) translate basic mental commands into computer-mediated actions, thereby allowing the user to bypass the peripheral motor system and interact with the world directly via brain activity. These systems are being developed to aid users with motor deficits stemming from neurodegenerative disease, injury, or even environmental restrictions which make movement difficult or impossible. One of the most successful classes of EEG-driven BCI systems is the P300, which works by detecting user responses to flashed stimuli. In most P300 systems, a grid of letters and/or other symbols is presented and rows or columns of the symbols are flashed in random order; the user attends to the desired symbol (usually by silently counting when it flashes). A major problem with these grid-based P300 systems is that the user must ideally look at the flashed target and minimally attend to the tiny letters, but late-stage ALS and other locked-in patients for whom these systems are most needed have trouble foveating targets and making controlled eye movements. The PI's hypothesis is that a BCI that flashes segments of one large letter can retain the combinatorial efficiency that comes with querying several letters at once, while having the advantage of one central focus (no gaze shifts required). This research aims to design and test this new segment speller idea. Project outcomes have the potential to vastly improve the usability of P300 EEG-based BCI systems for those with visual, sensory and motor impairments. All software written for EEG signal processing and analysis will be made available as add-ons to EEGLAB which is distributed by the Swartz Center for Computational Neuroscience (SCCN) at UCSD and part of the Temporal Dynamics of Learning Center. Data will also be made available through the HeadIT data archive that is also run by the SCCN.This research task can be broken down into three main objectives: develop and test the response to flashed segments; improve the single-trial classification of the responses to flashed segments; and design a logic for selecting segments and interpreting their responses. The developed system will provide another method for BCI speller control that does not depend on the ability to shift gaze. The PI argues that this method will have a higher information transfer rate than other space invariant BCI spellers due to being able to probe multiple letters at once. Besides being advantageous for those with impaired eye movements and/or impaired vision, the method should have other advantages over the standard P300 systems. When errors are made, they will tend to be to visually similar symbols. Incorporating language priors and active segment selection is easily accommodated, and this may result in higher information transfer rates with slower flash rates. In addition the work on improving recognition of single-trial temporal EEG signals and incorporating Bayesian language models into spellers could be useful for other types of brain-computer interfaces.