通过可穿戴设备EEG进行心理状态识别
Mental State Recognition via Wearable EEG
摘要：
消费者脑电图（EEG）耳机的质量和价格的不断提高使其在医疗级设备不切实际的情况下具有吸引力。预测和跟踪认知状态是可能的任务，以前不利于EEG监测。例如，监控操作员不适合任务的状态（例如昏昏欲睡的司机），跟踪心理健康（例如焦虑）和生产力（例如疲劳）都是该技术的可能应用。消费级EEG耳机价格实惠且相对易于使用，但它们缺乏使用医疗级EEG设备可以实现的信号分辨率和质量。因此，关键问题仍然存在：可穿戴EEG设备能够在多大程度上识别精神状态，以及这些设备可以准确识别什么样的精神状态？在这项工作中，我们研究了对两种不同类型输入的反应：教学（逻辑）与娱乐（情感）视频，使用一系列机器学习方法。我们尝试了支持向量机、稀疏逻辑回归和深度信念网络，来区分不同类型的视频输入所引起的心理状态，这些心理状态可以大致标记为逻辑与情感。我们的研究结果表明，可穿戴EEG设备在区分具有较大上下文但细微明显差异的情况之间的认知状态方面具有显着的潜力。
英文摘要：
The increasing quality and affordability of consumer electroencephalogram (EEG) headsets make them attractive for situations where medical grade devices are impractical. Predicting and tracking cognitive states is possible for tasks that were previously not conducive to EEG monitoring. For instance, monitoring operators for states inappropriate to the task (e.g. drowsy drivers), tracking mental health (e.g. anxiety) and productivity (e.g. tiredness) are among possible applications for the technology. Consumer grade EEG headsets are affordable and relatively easy to use, but they lack the resolution and quality of signal that can be achieved using medical grade EEG devices. Thus, the key questions remain: to what extent are wearable EEG devices capable of mental state recognition, and what kind of mental states can be accurately recognized with these devices? In this work, we examined responses to two different types of input: instructional (logical) versus recreational (emotional) videos, using a range of machine-learning methods. We tried SVMs, sparse logistic regression, and Deep Belief Networks, to discriminate between the states of mind induced by different types of video input, that can be roughly labeled as logical vs. emotional. Our results demonstrate a significant potential of wearable EEG devices in differentiating cognitive states between situations with large contextual but subtle apparent differences.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
