@inproceedings{670ba8b5752948498fd207b7861690ab,
title = "Study on EEG Channel Selection for Visual Manipulation Tasks",
abstract = "At present, electroencephalogram (EEG) has been widely used in the classification of mental workload. But most of the EEG acquisition devices used in the research a use a large number of electrodes. However, this brings high hardware costs, limited portability and discomfort to the wearer. In addition, most of the channels have information redundancy and noise interference, which have a negative impact on the subsequent mental workload classification. Therefore, it is necessary to use fewer channels to accurately identify the mental load of the operator. Focusing on the above problems, a method of channel selection based on Davies–Bouldin Index (DBI) for visual manipulation tasks is proposed in this paper, it selects effective channels by analyzing the differences between the features of low and high workload data.",
keywords = "Channel selection, Classification of mental workload, EEG, SVM",
author = "Hongquan Qu and Min Liu and Liping Pang and Hongbin Qu and Ling Wang",
note = "Publisher Copyright: {\textcopyright} 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.; 21st International Conference on Man-Machine-Environment System Engineering, MMESE 2021 ; Conference date: 23-10-2021 Through 25-10-2021",
year = "2022",
doi = "10.1007/978-981-16-5963-8\_40",
language = "英语",
isbn = "9789811659621",
series = "Lecture Notes in Electrical Engineering",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "278--284",
editor = "Shengzhao Long and Dhillon, \{Balbir S.\}",
booktitle = "Man-Machine-Environment System Engineering",
address = "德国",
}