Recognition of cognitive load with a stacking network ensemble of denoising autoencoders and abstracted neurophysiologic
- PDF / 1,966,982 Bytes
- 13 Pages / 595.276 x 790.866 pts Page_size
- 26 Downloads / 168 Views
(0123456789().,-volV)(0123456789(). ,- volV)
RESEARCH ARTICLE
Recognition of cognitive load with a stacking network ensemble of denoising autoencoders and abstracted neurophysiological features Zixuan Cao1,2 • Zhong Yin1,2
•
Jianhua Zhang3
Received: 30 December 2019 / Revised: 15 September 2020 / Accepted: 30 September 2020 Ó Springer Nature B.V. 2020
Abstract The safety of human–machine systems can be indirectly evaluated based on operator’s cognitive load levels at each temporal instant. However, relevant features of cognitive states are hidden behind in multiple sources of cortical neural responses. In this study, we developed a novel neural network ensemble, SE-SDAE, based on stacked denoising autoencoders (SDAEs) which identify different levels of cognitive load by electroencephalography (EEG) signals. To improve the generalization capability of the ensemble framework, a stacking-based approach is adopted to fuse the abstracted EEG features from activations of deep-structured hidden layers. In particular, we also combine multiple K-nearest neighbor and naive Bayesian classifiers with SDAEs to generate a heterogeneous classification committee to enhance ensemble’s diversity. Finally, we validate the proposed SE-SDAE by comparing its performance with mainstream pattern classifiers for cognitive load evaluation to show its effectiveness. Keywords Human–machine system Cognitive load Neurophysiological signals Electroencephalography Neural network
Introduction Human–machine (HM) systems widely exist in complex control environments for accomplishing predefined cognitive tasks (Habib et al. 2017). The HM systems have a capability to stabilize machine performance by incorporating actions of operator’s supervision and decisionmaking (Yin et al. 2015; Yin and Zhang 2017). Different from machine agents who possess reliable functionalities, operators’ performance can be instable or degraded & Zhong Yin [email protected] 1
Engineering Research Center of Optical Instrument and System, Ministry of Education, Shanghai Key Lab of Modern Optical System, University of Shanghai for Science and Technology, Jungong Road 516, Yangpu District, Shanghai 200093, People’s Republic of China
2
School of Optical-Electrical and Computer Engineering, University of Shanghai for Science and Technology, Jungong Road 516, Yangpu District, Shanghai 200093, People’s Republic of China
3
OsloMet Artificial Intelligence Lab, Department of Computer Science, Oslo Metropolitan University, 0130 Oslo, Norway
because of intention distraction, mental fatigue and mental overload (Rusnock and Borghetti 2018; Parasuraman and Jiang 2012). Such issues are major factors that cause many serious accidents originated by human operators. Numerous completed studies have shown that cognitive load is inversely related to the performance and operation quality of an operator in HM system (Lewis 2019). The concept of the cognitive load is closely linked to cognition frameworks, operator emotions, and mental demand (Lewis 2019). The distribution of t
Data Loading...
