WLnet: Towards an Approach for Robust Workload Estimation Based on Shallow Neural Networks

Electroencephalography (EEG) is a non-invasive technology used for the human brain-computer interface. One of its important applications is the evaluation of the mental state of an individual, such as workload estimation. In previous works, common spatial pattern feature extraction methods have been proposed for the EEG-based workload detection. Recently, several novel methods were introduced to detect EEG pattern workloads. However, it is still unknown which one of these methods is the one that offers the best performance for the workload EEG pattern feature detection. In this paper, four methods were used to extract workload EEG features: (a) common spatial pattern feature extraction; (b) temporally constrained sparse group spatial pattern feature extraction; (c) EEGnet; and (d) the new proposed shallow convolutional neural network for workload estimation (WLnet). The classification accuracy of these four methods was compared. Experimental results demonstrate that the proposed WLnet achieved the best detection accuracy in both stress and non-stress conditions. We believe that the proposed methods may be relevant to real-life applications of mental workload estimation.

Recommended citation: Zhe Sun, Binghua Li, Feng Duan, Hao Jia, Shan Wang, Yu Liu, Andrzej Cichocki, Cesar F. Caiafa, Jordi Sol ́e-Casals. WLnet: Towards an Approach for Robust Workload Estimation Based on Shallow Neural Networks. IEEE Access, 2020, 9: 3165-3173.
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