Decoding the cognitive states of attention and distraction in a real-life setting using EEG.

Kaushik, Pallavi; Moye, Amir; Vugt, Marieke van; Roy, Partha Pratim (2022). Decoding the cognitive states of attention and distraction in a real-life setting using EEG. Scientific Reports, 12(1), p. 20649. Nature Publishing Group 10.1038/s41598-022-24417-w

Preview

Text s41598-022-24417-w.pdf - Published Version Available under License Creative Commons: Attribution (CC-BY). Download (3MB) | Preview

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.

Interest & Impact

Downloads

26 since deposited on 01 Dec 2022
14 in the past 12 months

Citations

5 Citations in Web of Science ®
6 Citations in Scopus

Search

in Google Scholar™

Services

Actions (login required)

Edit item

Actions (login required)

Edit item