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

在加拿大，成千上万的人无法用语言或手势等传统方式与亲人交流。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，将独特的神经特征转化为预先定义的信息，从而为他们提供发声的机会。更具体地说，他们利用所谓的稳态视觉诱发电位（SSVEP）从脑电图（EEG）测量。他们的目标是为用户提供一种低成本、便携的解决方案，可以陪伴他们从家里到诊所，没有麻烦。然而，移动性和对低成本脑电图设备的依赖显著降低了信号质量并阻碍了系统性能。本项目旨在通过提出三个关键创新来填补这一空白：(1)一种新的自动化EEG增强算法，(2)一种新的EEG信号质量度量，以及(3)使用度量来提取更好的特征并设计质量感知的机器学习算法。当结合在一起时，增加eeg工件的鲁棒性将成为可能，从而减少系统假警报，并允许用户更有效地沟通，变得更加独立，并最终提高他们的生活质量。

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.