It is important to maintain attention when carrying out significant daily-life tasks that require high levels of safety and efficiency. Since degradation of attention can sometimes have dire consequences, various brain activity measurement devices such as electroencephalography (EEG) systems have been used to monitor attention states in individuals. However, conventional EEG instruments have limited utility in daily life because they are uncomfortable to wear. Thus, this study was designed to investigate the possibility of discriminating between the attentive and resting states using in-ear EEG signals for potential application via portable, convenient earphone-shaped EEG instruments. We recorded both on-scalp and in-ear EEG signals from 6 subjects in a state of attentiveness during the performance of a visual vigilance task. We have designed and developed in-ear EEG electrodes customized by modelling both the left and right ear canals of the subjects. We use an echo state network (ESN), a powerful type of machine learning algorithm, to discriminate attention states on the basis of in-ear EEGs. We have found that the maximum average accuracy of the ESN method in discriminating between attentive and resting states is approximately 81.16% with optimal network parameters. This study suggests that portable in-ear EEG devices and an ESN can be used to monitor attention states during significant tasks to enhance safety and efficiency.

中文翻译：

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使用回声状态网络的基于入耳式EEG的注意力状态分类。

在执行需要高水平安全性和效率的重要日常生活任务时，保持注意力很重要。由于注意力下降有时可能会带来可怕的后果，因此各种脑活动测量设备（例如脑电图（EEG）系统）已用于监视个人的注意力状态。然而，传统的EEG仪器由于不舒服而在日常生活中用途有限。因此，本研究旨在研究使用入耳式EEG信号来区分注意力状态和静息状态的可能性，以通过便携式，便捷的耳机形EEG仪器进行潜在应用。在执行视觉警戒任务期间，我们在注意状态下记录了6位受试者的头皮和耳内EEG信号。我们已经设计和开发了通过模拟对象的左耳道和右耳道而定制的入耳式EEG电极。我们使用回声状态网络（ESN）（一种功能强大的机器学习算法）来基于入耳式EEG区分注意力状态。我们发现，使用最佳网络参数，ESN方法在区分注意力状态和静止状态时的最大平均准确度约为81.16％。这项研究表明，便携式入耳式EEG设备和ESN可用于在重要任务期间监视注意力状态，以提高安全性和效率。根据入耳式脑电图区分注意力状态。我们发现，使用最佳网络参数，ESN方法在区分注意力状态和静止状态时的最大平均准确度约为81.16％。这项研究表明，便携式入耳式EEG设备和ESN可用于在重要任务期间监视注意力状态，以提高安全性和效率。根据入耳式脑电图区分注意力状态。我们发现，使用最佳网络参数，ESN方法在区分注意力状态和静止状态时的最大平均准确度约为81.16％。这项研究表明，便携式入耳式EEG设备和ESN可用于在重要任务期间监视注意力状态，以提高安全性和效率。