@inproceedings{44c57e980d0445a5b4fd29bc87a3a556,
title = "Detecting interruption events using EEG",
abstract = "Contemporary computing devices subject their users to continuous interruptions that can seriously harm productivity and well-being. Understanding how people react to notifications can provide valuable information in managing undesirable interruptions. We test whether a wearable EEG system can detect interruption decision events. Participants in a lab experiment (n=15) received notifications while carrying out a primary task, at the same time their brain activity was recorded with a wearable EEG system. We show that specific EEG features can distinguish between notifications that interrupt the user{\textquoteright}s activity and notifications that the user can disregard. Our results demonstrate that wearable EEG can serve as a basis for managing interruptions.",
keywords = "EEG, Interruption, Mental load, Workload",
author = "Frank Bolton and Dov Te{\textquoteright}Eni and Maimon, \{Neta B.\} and Eran Toch",
note = "Publisher Copyright: {\textcopyright} 2021 IEEE.; 3rd IEEE Global Conference on Life Sciences and Technologies, LifeTech 2021 ; Conference date: 09-03-2021 Through 11-03-2021",
year = "2021",
month = mar,
day = "9",
doi = "10.1109/LifeTech52111.2021.9391915",
language = "الإنجليزيّة",
series = "LifeTech 2021 - 2021 IEEE 3rd Global Conference on Life Sciences and Technologies",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "33--34",
booktitle = "LifeTech 2021 - 2021 IEEE 3rd Global Conference on Life Sciences and Technologies",
address = "الولايات المتّحدة",
}