Decoding imagined vowel productions using electroencephalography

使用脑电图解码想象的元音产生

• 批准号: 8335640
• 负责人: EMILY Patricia STEPHEN
• 金额: $ 3.41万
• 依托单位: BOSTON UNIVERSITY (CHARLES RIVER CAMPUS)
• 依托单位国家: 美国
• 财政年份: 2011
• 资助国家: 美国
• 起止时间: 2011-09-01 至 2014-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8335640
• 关键词: Algorithms; Anarthria speech disorder; Arts; Benchmarking; Classification; Communication; Cues; Data; Development; Devices; Electroencephalography; Ensure; Foundations; Future; Goals; Hand; Individual; Left; Locked-In Syndrome; Logistic Regressions; Methods; Movement; Neurologic; Outcome; Paralysed; Participant; Patients; Pattern; Performance; Process; Production; Quality of life; Research; Rest; Signal Transduction; Solid; Speech; Speed; Techniques; Testing; Time; Training; Validation; Work; analytical method; arm; base; brain computer interface; cognitive function; design; expectation; improved; limb movement; motor impairment; relating to nervous system; response; sound; spelling

DESCRIPTION (provided by applicant): One of the most important applications for Brain-Computer Interfaces (BCIs) is in individuals who are almost completely paralyzed but whose cortical processes are intact, such as patients with Locked-in Syndrome. For these people, communication can be laborious if not impossible without the aid of a BCI. Most current non-invasive communication BCIs use an indirect approach involving neural signals unrelated to speech to accomplish spelling or typing tasks. However, recent research into direct speech BCIs holds the promise of faster communication speeds because the decoding algorithms utilize neural activity related to more natural communication, thus reducing the burden of effort for the user. This study will take the first steps towards developing an electroencephalography (EEG) based direct real-time speech BCI to restore communication to severely impaired patients. Specifically, this study will develop the ability to detect when a user is attempting to operate the BCI device or is at rest and the ability to decode differences in EEG patterns of activity between imagined vowels. This will involve (1) developing and optimizing analytical methods to decode EEG signals resulting from imagined movements in a cued paradigm, (2) benchmarking the analytical methods against current methods in the field by applying them to well known imagined hand movement paradigms, (3) applying the analytical methods to speech-related movements (imagined vowel productions), and (4) extending the analytical methods to decode signals collected in a self-paced paradigm. In order to achieve these aims, this study will record EEG from healthy and paralyzed participants while at rest or imagining a hand movement or vowel production in response to a cue. Two primary features will be used to decode the EEG signals: (1) the amplitude of the sensorimotor rhythms (SMR) in the mu, beta, and gamma bands, and (2) the amplitude of the signal transformed using the first and last two Common Spatial Patterns (CSPs) associated with the different experimental conditions. A number of classifiers will be applied to the data offline to decode imagined movements from each other and from rest. Classifier performance will be determined through cross-validation. Once the classifiers have been trained on the cued paradigm, they will be applied offline to a self-paced paradigm in which participants are given a fixed amount of time to imagine repeating a movement a given number of times. The classifiers will be judged based on how well they can predict the number of times each movement was repeated. The same analytical methods and experimental paradigms will be applied to both imagined hand movements (left or right fist clenching vs. rest) and to imagined vowel productions (/a/ or /u/ vs. rest). In a preliminary study in which one participant performed the cued paradigm, a 31-parameter logistic regression classifier was able to classify right vs. rest, left vs. rest, right vs. left, /a/ vs. rest, /u/ vs. rest, and /a/ vs. /u/ at levels above chance for the testing data, illustrating the feasibility of the proposed research.

描述（由申请人提供）：脑部计算机界面（BCIS）的最重要应用之一是几乎完全瘫痪但其皮质过程完好无损的个体，例如锁定综合征的患者。对于这些人来说，如果没有BCI的帮助，沟通也可能是费力的，即使不是不可能的。大多数当前的非侵入性通信BCI都使用涉及与语音无关的神经信号的间接方法来完成拼写或打字任务。但是，最近对直接语音的研究具有更快的通信速度的希望，因为解码算法利用与更自然的交流有关的神经活动，从而减轻了用户的努力负担。这项研究将采取第一步，以开发基于脑电图（EEG）的直接实时语音BCI，以恢复与严重受损患者的沟通。具体而言，这项研究将发展能够检测用户何时尝试操作BCI设备或静止的能力，并能够解码想象中的元音之间的脑电图模式差异。 This will involve (1) developing and optimizing analytical methods to decode EEG signals resulting from imagined movements in a cued paradigm, (2) benchmarking the analytical methods against current methods in the field by applying them to well known imagined hand movement paradigms, (3) applying the analytical methods to speech-related movements (imagined vowel productions), and (4) extending the analytical methods to decode signals collected在一个自节奏的范式中。为了实现这些目标，本研究将在休息时记录健康和瘫痪的参与者的脑电图，或者想象一下手动运动或元音生产以响应提示。将使用两个主要功能来解码EEG信号：（1）MU，Beta和Gamma频段中感觉运动节律（SMR）的振幅，以及（2）使用与不同实验条件相关的第一个和最后两个常见的空间模式（CSP）转换信号的幅度。许多分类器将被应用于数据离线，以从彼此和休息中解码想象的运动。分类器性能将通过交叉验证确定。一旦对提示范式进行了分类器的培训，它们将离线应用于自节奏范式，在该范式中，参与者有固定的时间来想象将动作重复给定的次数。分类器将根据他们可以预测每个运动的重复次数的次数来判断分类器。相同的分析方法和实验范例将应用于想象中的手动运动（左右或右拳头与休息）和想象中的元音生产（/ a/ a/ or/ u/ vs. elts）。在一项初步的研究中，一个参与者执行了提示范式，31参数的逻辑回归分类器能够对右对休息进行分类，左与休息，右与左右， / a / a / a / a / a / u / vs. vs.和 / a / a / a / a / a / vs. / vs. / vs. / u / vs. / u / vs. / u / vs. / u / vs. / u / vs. / u / vs.在测试数据上的可能性上面的级别上，则说明了该测试数据的机会，说明了拟议的研究。