WalkingWizard - A truly wearable EEG headset for everyday use

Teck Lun Goh, L. Peh
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Abstract

Electroencephalography (EEG) provides an opportunity to gain insights to electrocortical activity without the need for invasive technology. While increasingly used in various application areas, EEG headsets tend to be suited only to a laboratory environment due to the long preparation time to don the headset and the need for users to remain stationary. We present our design of a dry, dual-electrodes flexible PCB assembly that realizes accurate sensing in face of practical motion artifacts. Using it, we present WalkingWizard, our prototype dry-electrode EEG baseball cap that can be used under motion in everyday scenarios. We first evaluated its hardware performance by comparing its electrode-scalp impedance and ability to capture alpha rhythm against both wet EEG, and commercially available dry EEG headsets. We then tested WalkingWizard using SSVEP experiments, achieving high classification accuracy of 87% for walking speeds up to 5.0km/hr, beating state-of-the-art. Expanding on WalkingWizard, we integrated all necessary electronic components into a flexible PCB assembly - realizing WalkingWizard Integrated , in a truly wearable form-factor. Utilizing WalkingWizard Integrated, we demonstrated several applications as proof-of-concept: Classification of SSVEP in VR environment while walking, Real-time acquisition of emotional state of users while moving around the neighbourhood, and Understanding the effect of guided meditation for relaxation.
WalkingWizard - 适合日常使用的真正可佩戴脑电图耳机
脑电图(EEG)提供了一个无需侵入性技术即可深入了解皮层电活动的机会。虽然脑电图耳机越来越多地应用于各个领域,但由于佩戴耳机的准备时间较长,而且用户需要保持静止不动,因此往往只适用于实验室环境。我们介绍了我们设计的干式双电极柔性 PCB 组件,它能在实际运动伪影面前实现精确传感。利用它,我们推出了 WalkingWizard,这是我们的干电极脑电图棒球帽原型,可在日常运动场景下使用。我们首先评估了它的硬件性能,将其电极鳞片阻抗和捕捉α节律的能力与湿式脑电图和市售干式脑电图耳机进行了比较。然后,我们使用 SSVEP 实验对 WalkingWizard 进行了测试,在步行速度高达 5.0km/hr 的情况下,分类准确率高达 87%,超过了最先进的水平。在 WalkingWizard 的基础上,我们将所有必要的电子元件集成到一个灵活的印刷电路板组件中--实现了 WalkingWizard Integrated,具有真正的可穿戴外形。利用 WalkingWizard Integrated,我们展示了几个应用作为概念验证:步行时在 VR 环境中对 SSVEP 进行分类、在社区中移动时实时获取用户的情绪状态,以及了解引导式冥想对放松的影响。
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来源期刊
CiteScore
10.30
自引率
0.00%
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