Department of Computer Science and Engineering, Indian Institute of Technology, Roorkee, 247667, India.
Bernoulli Institute of Mathematics, Computer Science and Artificial Intelligence, University of Groningen, 9700, AK, Groningen, the Netherlands.
Sci Rep. 2022 Nov 30;12(1):20649. doi: 10.1038/s41598-022-24417-w.
Lapses in attention can have serious consequences in situations such as driving a car, hence there is considerable interest in tracking it using neural measures. However, as most of these studies have been done in highly controlled and artificial laboratory settings, we want to explore whether it is also possible to determine attention and distraction using electroencephalogram (EEG) data collected in a natural setting using machine/deep learning. 24 participants volunteered for the study. Data were collected from pairs of participants simultaneously while they engaged in Tibetan Monastic debate, a practice that is interesting because it is a real-life situation that generates substantial variability in attention states. We found that attention was on average associated with increased left frontal alpha, increased left parietal theta, and decreased central delta compared to distraction. In an attempt to predict attention and distraction, we found that a Long Short Term Memory model classified attention and distraction with maximum accuracy of 95.86% and 95.4% corresponding to delta and theta waves respectively. This study demonstrates that EEG data collected in a real-life setting can be used to predict attention states in participants with good accuracy, opening doors for developing Brain-Computer Interfaces that track attention in real-time using data extracted in daily life settings, rendering them much more usable.
注意力不集中在开车等情况下可能会产生严重后果，因此，人们非常有兴趣使用神经测量来跟踪注意力。然而，由于这些研究大多数都是在高度控制和人为的实验室环境中进行的，我们想探索是否也可以使用机器学习/深度学习来确定自然环境中脑电图 (EEG) 数据中的注意力和分心。24 名参与者自愿参加了这项研究。数据是从同时参与藏传佛教辩论的成对参与者那里收集的，这种做法很有趣，因为它是一种现实生活中的情况，会导致注意力状态发生很大变化。我们发现，与分心相比，注意力平均与左额 alpha 增加、左顶 theta 增加和中央 delta 减少有关。为了尝试预测注意力和分心，我们发现长短期记忆模型对注意力和分心的分类准确率最高，分别为 95.86%和 95.4%，对应于 delta 和 theta 波。这项研究表明，在真实环境中收集的 EEG 数据可以用于以较高的准确率预测参与者的注意力状态，为开发使用日常生活环境中提取的数据实时跟踪注意力的脑机接口打开了大门，使其更加可用。
