Distinguishing mental attention states of humans via an EEG-based passive BCI using machine learning methods

作者：

Highlights：

• Three mental attention states (focused, unfocused, sleep) are diagnosed using EEG.

• 25 hours of EEG data from 5 participants are collected.

• A classic Emotiv headset is modified.

• SVM, kNN and ANFIS methods were used for classification.

• 96.70% (best) and 91.72% (avg.) accuracy are obtained from SVM.

摘要

•Three mental attention states (focused, unfocused, sleep) are diagnosed using EEG.•25 hours of EEG data from 5 participants are collected.•A classic Emotiv headset is modified.•SVM, kNN and ANFIS methods were used for classification.•96.70% (best) and 91.72% (avg.) accuracy are obtained from SVM.

论文关键词：EEG,BCI,Mental state detection,Drowsiness detection,Support vector machine,Passive control task

论文评审过程：Received 30 January 2019, Revised 13 May 2019, Accepted 30 May 2019, Available online 30 May 2019, Version of Record 6 June 2019.

论文官网地址：https://doi.org/10.1016/j.eswa.2019.05.057