3D CNN based Multilevel Feature Fusion for Workload Estimation

Youngchul Kwak, Woo Jin Song, Byoung Kyong Min, Seong Eun Kim

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Mental workload is defined as the proportion of the information processing capability used to perform a task. High cognitive load requires additional resources that may reduce the processing efficiency and performance. Therefore, the technique of workload estimation can ensure a proper working environment to promote the working efficiency of each person. In this paper, we propose a three-dimensional convolutional neural network (3D CNN) based a multilevel feature fusion algorithm for mental workload estimation using electroencephalogram (EEG) signals. Raw EEG signals are converted to 3D EEG images and then multilevel features are extracted in each layer of the 3D convolution operation and each multilevel feature is multiplied by a weighting factor, which determines the importance of the feature. The accuracy of our network is 90.3%, which is better than conventional algorithms.

Original languageEnglish
Title of host publication8th International Winter Conference on Brain-Computer Interface, BCI 2020
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728147079
ISBN (Print)9781728147079
DOIs
Publication statusPublished - 2020 Feb 1
Event8th International Winter Conference on Brain-Computer Interface, BCI 2020 - Gangwon, Korea, Republic of
Duration: 2020 Feb 262020 Feb 28

Publication series

Name8th International Winter Conference on Brain-Computer Interface, BCI 2020

Conference

Conference8th International Winter Conference on Brain-Computer Interface, BCI 2020
CountryKorea, Republic of
CityGangwon
Period20/2/2620/2/28

Keywords

  • Convolutional neural network
  • electroencephalogram (EEG)
  • mental workload

ASJC Scopus subject areas

  • Behavioral Neuroscience
  • Cognitive Neuroscience
  • Artificial Intelligence
  • Human-Computer Interaction

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  • Cite this

    Kwak, Y., Song, W. J., Min, B. K., & Kim, S. E. (2020). 3D CNN based Multilevel Feature Fusion for Workload Estimation. In 8th International Winter Conference on Brain-Computer Interface, BCI 2020 [9061639] (8th International Winter Conference on Brain-Computer Interface, BCI 2020). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/BCI48061.2020.9061639