Browsing by Author "Wang, Qi"

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    Characterisation of cognitive load using machine learning classifiers of electroencephalogram data
    (MDPI, 2023-10-17) Wang, Qi; Smythe, Daniel; Cao, Jun; Hu, Zhilin; Proctor, Karl J.; Owens, Andrew P.; Zhao, Yifan
    A high cognitive load can overload a person, potentially resulting in catastrophic accidents. It is therefore important to ensure the level of cognitive load associated with safety-critical tasks (such as driving a vehicle) remains manageable for drivers, enabling them to respond appropriately to changes in the driving environment. Although electroencephalography (EEG) has attracted significant interest in cognitive load research, few studies have used EEG to investigate cognitive load in the context of driving. This paper presents a feasibility study on the simulation of various levels of cognitive load through designing and implementing four driving tasks. We employ machine learning-based classification techniques using EEG recordings to differentiate driving conditions. An EEG dataset containing these four driving tasks from a group of 20 participants was collected to investigate whether EEG can be used as an indicator of changes in cognitive load. The collected dataset was used to train four Deep Neural Networks and four Support Vector Machine classification models. The results showed that the best model achieved a classification accuracy of 90.37%, utilising statistical features from multiple frequency bands in 24 EEG channels. Furthermore, the Gamma and Beta bands achieved higher classification accuracy than the Alpha and Theta bands during the analysis. The outcomes of this study have the potential to enhance the Human–Machine Interface of vehicles, contributing to improved safety.
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    Data: Characterisation of Cognitive Load using Machine Learning Classifiers of Electroencephalogram Data
    (Cranfield University, 2023-08-17 14:40) Wang, Qi
    A high cognitive load can overload a person resulting in catastrophic accidents, it is therefore important to ensure the cognitive load of a safety-critical task (such as driving a vehicle) is at a manageable level. Although electroencephalography (EEG) has attracted significant interest in the research of cognitive load, few studies use EEG to investigate driving-related cognitive load. This paper presents a feasibility study on the simulation of various levels of cognitive load through designing and implementing four driving tasks, and the associated classification of load using EEG recordings. An EEG dataset containing these four driving tasks from a group of 20 participants was collected to investigate whether EEG can be used as a biomarker to reflect changes in cognitive load. The dataset was then used to train four Deep Neural Networks (DNNs) and four Support Vector Machines (SVMs) classification models. The results showed that the best model achieved a classification accuracy of 90.37% utilizing statistical features from multiple frequency bands in 24 EEG channels. Furthermore, it was observed that the Gamma and Beta bands achieved a greater classification accuracy than the Alpha and Theta bands. The output of this study can potentially improve the Human-Machine-Interface of vehicles for enhanced safety.
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    Network analysis of water-related ecosystem services in search of solutions for sustainable catchment management: A case study in Sutlej-Beas River systems, India
    (Elsevier, 2023-08-31) Yu, Shuying; Peng, Jian; Xia, Pei; Wang, Qi; Grabowski, Robert C.; Azhoni, Adani; Bala, Brij; Shankar, Vijay; Meersmans, Jeroen
    Hydrological processes and ecosystem interactions are instrumental in sustaining local populations by providing various water-related ecosystem services (ES). Numerous studies gave priority to the theories and methods of building networks that emphasized different stakeholders. However, little study has examined the complex relationships among water-related ES themselves and how relevant human activities affect ES networks. To narrow this gap, in this study we quantified four critical water-related ES (flood mitigation, hydropower production, soil retention, and water conservation), set up six ES network types based on the synergy relationship, and further explored the effect of human activities on these networks. The results showed that among six ES network categories, networks with four fully linked ES occupied a large percentage of 23.20% while the network with one central ES linking two others accounted for the lowest percentage (9.28%). Compared with other ES, soil retention tended to be less centralized within the networks. In addition, land use intensity was found to greatly influence the ES networks compared with other indicators, especially for less complex networks. Our results highlighted the importance of network analysis in searching solutions for sustainable catchment management.