Detecting user attention to video segments using interval EEG features

作者：

Highlights：

• A method of detecting the top 20% of viewer attention to video segments is proposed.

• This is the first study of detecting viewer attention during video viewing.

• All subject-independent models unbiased to specific genres are evaluated.

• The all-14-channel, single-channel, and selected multi-channel models are included.

• The interval band ratio features are the most suitable for all the types of models.

摘要

•A method of detecting the top 20% of viewer attention to video segments is proposed.•This is the first study of detecting viewer attention during video viewing.•All subject-independent models unbiased to specific genres are evaluated.•The all-14-channel, single-channel, and selected multi-channel models are included.•The interval band ratio features are the most suitable for all the types of models.

论文关键词：Detection,User attention,Video viewing,Video segments,Interval EEG features

论文评审过程：Received 30 November 2017, Revised 20 July 2018, Accepted 10 August 2018, Available online 11 August 2018, Version of Record 5 September 2018.

论文官网地址：https://doi.org/10.1016/j.eswa.2018.08.016