@inproceedings{f9bd885240704afb9314ada5ad4d7936,
title = "Using Noninvasive Neural Signal to Recognize Single- and Multi-task States of Operators",
abstract = "In this paper, we propose an electroencephalogram (EEG) signals-based method to recognize single- and multi-task states of users by using the linear discriminant analysis (LDA) algorithm and convolutional neural network (CNN). A novel experimental paradigm is designed to validate the proposed method. Experimental results from eight subjects show that the proposed methods perform well. Furthermore, the average accuracy of the recognition model based on CNN reaches 89.13% and is 5% higher than that based on the LDA algorithm. This work not only lays a foundation for the development of adaptive assistant systems based on brain-computer interfaces, but it also advances the study of human state monitoring and human-machine interaction based on EEG signals.",
keywords = "Electroencephalogram (EEG) signals, assistive control, brain-computer interface (BCI), convolutional neural network (CNN), single- and multi-task states",
author = "Shengchao Xia and Luzheng Bi and Xiaoguang Wang",
note = "Publisher Copyright: {\textcopyright} 2020 Technical Committee on Control Theory, Chinese Association of Automation.; 39th Chinese Control Conference, CCC 2020 ; Conference date: 27-07-2020 Through 29-07-2020",
year = "2020",
month = jul,
doi = "10.23919/CCC50068.2020.9188758",
language = "English",
series = "Chinese Control Conference, CCC",
publisher = "IEEE Computer Society",
pages = "3040--3043",
editor = "Jun Fu and Jian Sun",
booktitle = "Proceedings of the 39th Chinese Control Conference, CCC 2020",
address = "United States",
}