Cognitive state recognition using wavelet singular entropy and ARMA entropy with AFPA optimized GP classification

Cai, Zhengxiang; Wu, Qi; Huang, Dan; Ding, Lu; Yu, Biting; Law, Rob; Huang, Jiayang; Fu, Shan

doi:10.1016/j.neucom.2016.01.054

Cited by 9 publications

(3 citation statements)

References 43 publications

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“…Some scholars simply study the physiological, psychological, and physical characteristics separately. For example, in the physiological characteristics of pilots, some scholars have studied the vestibular sensation, vision, heart rate, and breathing status [9,12,14,16,17]. In terms of psychological characteristics, some scholars have studied the pilot's attention, situational awareness, cognitive load, etc.…”

Section: Discussionmentioning

confidence: 99%

“…Relevant scholars at home and abroad mainly study the problems faced by pilots in the cognitive process. Zhengxiang et al [14] used the six-degree-offreedom flight simulator to obtain physiological signals such as heart rate (HR), respiratory depth (RD), respiratory rate (RR), scan rate (SR), and pupil diameter (PD). Meantime, they also identified the cognitive processes at different flight stages.…”

Section: Literature Reviewmentioning

confidence: 99%

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Research on Influencing Factor Selection of Pilot’s Intention

Wang

Pan

et al. 2020

International Journal of Aerospace Engineering

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The physiological, psychological, and physical characteristics of the pilot will have an impact on flight safety, mainly in the pilot’s intention. In another word, this means the pilot’s psychological experience of flight status under the influence of various factors and the preference for decision-making or behavioral value that is displayed. The pilot’s intention is to reflect the cognitive state that the pilot showed during the maneuvering of the aircraft. The exploration of intention is very important for the study of automatic pilot and flight control active safety system. Also, it is an important concept often involved in the study of human factors in flight, especially the microbehavior of pilots. Pilot’s intention is taken as the study object in this paper; physiological-psychological-physical parameters are obtained through analyzing their influencing factors from the simulating flight experiments designed. The random forest analysis method is used to rank the main influencing factors affecting the pilot’s intention, and the factor sequence is formed. The results provide a good foundation for further research on the pilot’s intention identification.

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Section: Discussionmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Research on Influencing Factor Selection of Pilot’s Intention

Wang

Pan

et al. 2020

International Journal of Aerospace Engineering

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“…l) Respiration rate (RR) increases as MWL increases, but also depends on physical activity [38]. m) Saccadic rate (SR) brain commands sent to the ocular muscles result in the eyes doing a rapid staggered rotation after which the eyes remain stationary in their new position, these are known as saccadic eye movements that increase with MWL [46]. n) Systolic blood pressure (SBP) increases with work task difficulty [43].…”

Section: Physiological Mwl Measurementsmentioning

confidence: 99%

Real-time mental workload measurement for four break configurations in cognitive tasks using the EEG-based workload index (EWI)

Arana-De-Las-Casas,

Sáenz-Zamarrón,

Maldonado-Macías

et al. 2024

Preprint

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The current real tasks in work environments are complex, they demand a large amount of cognitive work, which causes the need to carry out Mental Workload (MWL) studies during the execution of those tasks. In high-tech manufacturing processes, the human-machine systems adopted are technologically more complex than ever and, consequently, the MWL of employees has increased during their work activities. A high MWL can adversely affect employee well-being, causing new health problems, mainly cognitive. MWL has become a relevant workplace concern due to its relationship with poor performance, low productivity, human errors, accidents, and stress. In this project, a design of experiments framework was created to measure MWL through a graphical user interface (GUI) that contains the Dual N-back method, it is developed by subjects while they performed a set of cognitive tasks; and it is measured physiologically by electroencephalogram (EEG). The design integrated hardware (Cyton© card as EEG measurement equipment) and software (OpenBCI, MATLAB, EEGLAB) that automatically synchronizes the electroencephalogram (EEG) with the activity performed (stimulus), this was done by using the Lab Streaming Layer (LSL)© system, which allows real-time data transmission between applications in a computer. This study validated the EWI model as an index of MWL performance.

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