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CHS: Small: A Novel P300 Brain-Computer Interface

CHS：小型：新型 P300 脑机接口

基本信息

批准号：
1528214
负责人：
Virginia de Sa
金额：
$ 49.96万
依托单位：
University of California-San Diego
依托单位国家：
美国
项目类别：
Continuing Grant
财政年份：
2015
资助国家：
美国
起止时间：
2015-10-01 至 2021-09-30
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1528214&HistoricalAwards=false
关键词：
CHS Small Novel P300 Brain

项目摘要

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.

脑机接口（BCI）将基本的心理命令转化为计算机介导的动作，从而允许用户绕过外围运动系统并通过大脑活动直接与世界互动。这些系统正在开发，以帮助用户与运动缺陷源于神经退行性疾病，损伤，甚至环境限制，使运动困难或不可能。 EEG驱动的BCI系统中最成功的一类是P300，它通过检测用户对闪光刺激的反应来工作。在大多数P300系统中，字母和/或其他符号的网格被呈现，并且符号的行或列以随机顺序闪烁;用户关注所需的符号（通常通过在闪烁时默默计数）。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，并且最低限度地关注小字母，但是最需要这些系统的晚期ALS和其他闭锁患者在注视目标和进行受控眼球运动方面有困难。 PI的假设是，闪烁一个大字母的片段的BCI可以保持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（不需要凝视转移）。本研究旨在设计和测试这种新的分段拼写器的想法。项目成果有可能大大提高P300 EEG为基础的BCI系统的视觉，感觉和运动障碍的可用性。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由UCSD的Swartz计算神经科学中心（SCCN）和学习中心的时间动力学的一部分分发。该研究任务可分为三个主要目标：开发和测试对闪现片段的响应;改进对闪现片段的响应的单次试验分类;以及设计用于选择片段和解释其响应的逻辑。开发的系统将为BCI拼写控制提供另一种方法，该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，这种方法将具有比其他空间不变BCI拼写器更高的信息传输速率。除了对那些眼睛运动受损和/或视力受损的人有利之外，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。很容易适应语言优先级和活动段选择，这可能会导致更高的信息传输速率和更慢的闪存速率。此外，改进单次试验时间EEG信号的识别以及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。

项目成果

期刊论文数量（8）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

EEG Reveals Familiarity by Controlling Confidence in Memory Retrieval

脑电图通过控制记忆检索的置信度来揭示熟悉程度

DOI：
发表时间：
2021
期刊：
Proceedings of the Annual Meeting of the Cognitive Science Society
影响因子：
0
作者：
Liao, Kueida;Mollison, Matthew V;Curran, Tim;de Sa, Virginia R
通讯作者：
de Sa, Virginia R

Personalized Pain Detection in Facial Video with Uncertainty Estimation

DOI：
10.1109/embc46164.2021.9631056
发表时间：
2021-11
期刊：
2021 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC)
影响因子：
0
作者：
Xiaojing Xu;V. D. Sa
通讯作者：
Xiaojing Xu;V. D. Sa

Multi-Subject Unsupervised Transfer with Weighted Subspace Alignment for Common Spatial Patterns