EEG enhancement and context-awareness for robust SSVEP-based brain-computer interfaces

脑电图增强和情境感知，用于基于 SSVEP 的稳健脑机接口

• 批准号: 521743-2017
• 负责人: Falk, Tiago
• 金额: $ 1.82万
• 依托单位: Institut national de la recherche scientifique
• 依托单位国家: 加拿大
• 项目类别: Engage Grants Program
• 财政年份: 2017
• 资助国家: 加拿大
• 起止时间: 2017-01-01 至 2018-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=639459
• 关键词: EEG; enhancement; context; awareness; robust

In Canada, hundreds of thousands of individuals are not able to communicate with their loved ones usingconventional methods such as speech or gestures. This can be due to severe disabilities, locked-in syndrome,neurodegenerative diseases such as amyotrophic lateral sclerosis (ALS), or autism spectrum disorders.Cognixion aims at giving these individuals a voice by tapping into their brain waves and converting uniqueneural signatures into pre-defined messages. More specifically, they utilize so-called steady-state visuallyevoked potentials (SSVEP) measured from electroencephalography (EEG). Their goal is to provide users witha low-cost, portable solution that can accompany them from home to the clinic without hassles. Mobility andreliance on low-cost EEG devices, however, significantly reduce signal quality and hamper systemperformance. This project aims to fill this gap by proposing three key innovations: (1) a new automated EEGenhancement algorithm, (2) a new EEG signal quality metric, and (3) the use of the metric to extract betterfeatures and to devise quality-aware machine learning algorithms. When combined, increased robustness toEEG artefacts will be possible, thus reducing system false alarms and allowing users to communicate moreeffectively, become more independent, and ultimately, improve their quality of life.

在加拿大，数十万人无法使用语音或手势等传统方法与亲人交流。这可能是由于严重残疾、闭锁综合征、神经退行性疾病，如肌萎缩侧索硬化症(ALS)或自闭症谱系障碍。Cognixion旨在通过利用这些人的脑电波并将独特的原始签名转换为预定义的信息来给予这些人发言权。更具体地说，他们利用从脑电(EEG)测量的所谓稳态视觉诱发电位(SSVEP)。他们的目标是为用户提供一种低成本、便携的解决方案，可以毫不费力地陪伴他们从家里到诊所。然而，机动性和对低成本脑电设备的依赖会显著降低信号质量并阻碍系统性能。本项目旨在通过提出三个关键创新来填补这一空白：(1)一种新的自动EEG增强算法，(2)一种新的EEG信号质量度量，以及(3)使用该度量来提取更好的特征并设计出质量感知的机器学习算法。当两者结合在一起时，将有可能增强对脑电伪影的稳健性，从而减少系统错误警报，并允许用户更有效地沟通，变得更加独立，最终提高他们的生活质量。