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.
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Qu, H., Liu, M., Pang, L., Qu, H., Wang, L. (2022). Study on EEG Channel Selection for Visual Manipulation Tasks. In: Long, S., Dhillon, B.S. (eds) Man-Machine-Environment System Engineering: Proceedings of the 21st International Conference on MMESE. MMESE 2021. Lecture Notes in Electrical Engineering, vol 800. Springer, Singapore. https://doi.org/10.1007/978-981-16-5963-8_40
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DOI: https://doi.org/10.1007/978-981-16-5963-8_40
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