EEG based Mental Workload Assessment via a Hybrid Classifier of Extreme Learning Machine and Support Vector Machine

Gu, Hanming; Yin, Zhong; Zhang, Jianhua

doi:10.23919/chicc.2019.8865496

Cited by 4 publications

(1 citation statement)

References 17 publications

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“…These traditional machine learning classifiers can be combined to improve classification performance. For example, Gu et al combined Extreme Learning Machine (ELM) and Support Vector Machine (SVM) to develop the ELM-SVM model, which achieved higher accuracy than separate classifiers (i.e., the single SVM and the single ELM) in the mental workload classification [20]. Besides, deep learning models were also used for workload classification.…”

Section: Introductionmentioning

confidence: 99%

Latent Space Coding Capsule Network for Mental Workload Classification

Yu,

Bezerianos,

Cichocki

et al. 2023

IEEE Trans. Neural Syst. Rehabil. Eng.

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Mental workload can be monitored in real time, which helps us improve work efficiency by maintaining an appropriate workload level. Based on previous studies, we have known that features, such as band power and brain connectivity, can be utilized to classify the levels of mental workload. As band power and brain connectivity represent different but complementary information related to mental workload, it is helpful to integrate them together for workload classification. Although deep learning models have been utilized for workload classification based on EEG, the classification performance is not satisfactory. This is because the current models cannot well tackle variances in the features extracted from non-stationary EEG. In order to address this problem, we, in this study, proposed a novel deep learning model, called latent space coding capsule network (LSCCN). The features of band power and brain connectivity were fused and then modelled in a latent space. The subsequent convolutional and capsule modules were used for workload classification. The proposed LSCCN was compared to the state-of-the-art methods. The results demonstrated that the proposed LSCCN was superior to the compared methods. LSCCN achieved a higher testing accuracy with a relatively smaller standard deviation, indicating a more reliable classification across participants. In addition, we explored the distribution of the features and found that top discriminative features were localized in the frontal, parietal, and occipital regions. This study not only provides a novel deep learning model but also informs further studies in workload classification and promotes practical usage of workload monitoring.

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Section: Introductionmentioning

confidence: 99%