Applications of electroencephalography in construction

Saedi, Sina; Fini, Alireza Ahmadian Fard; Khanzadi, Mostafa; Wong, Johnny; Sheikhkhoshkar, Moslem; Banaei, Maryam

doi:10.1016/j.autcon.2021.103985

Cited by 40 publications

(19 citation statements)

References 87 publications

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“…Furthermore, in the field of EEG‐based sensor system applications, feature extraction and classification are crucial steps following signal acquisition and preprocessing. Traditionally, EEG studies in construction have utilized various types of features, including time‐domain features (e.g., average amplitude), frequency‐domain features (e.g., power spectral density in specific frequency bands), and time‐frequency domain features (e.g., valence and arousal) as inputs to machine learning classifiers such as linear discriminant analysis, support vector machines, and k‐nearest neighbor (Jeon & Cai, 2021; Saedi et al., 2022). These approaches have shown impressive performance, but they require expertise in both EEG signal features and the specific engineering task being investigated, posing a technical barrier that may impede the future exploration of EEG‐based applications.…”

Section: Related Workmentioning

confidence: 99%

Brain‐regulated learning for classifying on‐site hazards with small datasets

Zhou

2023

Computer aided Civil Eng

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Machine vision technologies have the potential to revolutionize hazard inspection, but training machine learning models requires large labeled datasets and is susceptible to biases. The lack of robust perception capabilities in machine vision systems for construction hazard inspection poses significant safety concerns. To address this, we propose a novel method that leverages human knowledge extracted from electroencephalogram (EEG) recordings to enhance machine vision through transfer learning. By pretraining convolutional neural networks with EEG data recorded during construction hazard evaluations, we investigated three common on‐site hazard classifications using small datasets. Our results demonstrated that the proposed method resulted in improved accuracy (with an 11% increase) and enhanced rationality of machine learning predictions (as revealed by network visualization analysis). This research opens avenues for further exploration and industry applications, aiming to achieve more intelligent and human‐like artificial visual perception, ultimately enhancing safety and efficiency in automated hazard inspection.

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Section: Related Workmentioning

confidence: 99%

Brain‐regulated learning for classifying on‐site hazards with small datasets

Zhou

2023

Computer aided Civil Eng

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“…Chen et al (2017) investigated construction workers' mental workload using the EEG approach and demonstrated that the technique is effective. In the development of computing platforms and sensing technologies, the EEG system has been designed as portable, mobile, and affordable (Saedi et al, 2022). Wearable Mobile EEG devices were utilized to assess workers' psychosocial status, such as emotional states (Hwang et al, 2018), stress recognition (Jebelli et al, 2018b;Jebelli, Khalili, et al, 2018), physical and mental fatigue (Li et al, 2019;Xing et al, 2020), vigilance (D. Wang et al, 2017Wang et al, , 2019, and distraction (Ke et al, 2021) in construction.…”

Section: The Use Of Eeg Technology In the Construction Researchmentioning

confidence: 99%

“…Wearable Mobile EEG devices were utilized to assess workers' psychosocial status, such as emotional states (Hwang et al, 2018), stress recognition (Jebelli et al, 2018b;Jebelli, Khalili, et al, 2018), physical and mental fatigue (Li et al, 2019;Xing et al, 2020), vigilance (D. Wang et al, 2017Wang et al, , 2019, and distraction (Ke et al, 2021) in construction. Moreover, researchers have also used the EEG technique to investigate the risk perception of construction workers and other factors affecting their performance, such as construction activities, work conditions, tasks, and working hours (Liu et al, 2013;Saedi et al, 2022;Wang et al, 2017;Zhang et al, 2019). The aforementioned literature has shown the use of EEG is sufficient to explore the construction workers' mental states and cognition.…”

Section: The Use Of Eeg Technology In the Construction Researchmentioning

confidence: 99%

Neural mechanisms behind semantic congruity of construction safety signs: An EEG investigation on construction workers

et al. 2022

Hum Ftrs & Erg Mfg Svc

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Accurate comprehension of safety signs plays a critical part in warning construction workers of potential work hazards. However, existing studies have rarely investigated construction workers' comprehension of safety signs at construction sites. Moreover, existing evaluation methods are generally based on subjective behavior tests, questionnaires, and interviews. Therefore, this study examined the effects of semantic congruity on the comprehension of safety signs, including two sign types (prohibition vs. warning signs) and two conditions (semantic congruence vs. incongruence), combining event‐related potentials and time‐frequency analysis measurements. Adopting the S1‐S2 paradigm, electroencephalogram data were recorded when participants decided whether S1 and S2 were semantically congruent or not. Results showed that the semantically incongruent safety sign‐word pairs elicited larger N400 amplitudes and increased theta (3–8 Hz) power in 300–420 ms. The amplitude of N400 in the semantically incongruent condition of the warning sign‐word pairs was more negative than that for the prohibition sign‐word pairs, while there were no significant differences between the prohibition and warning sign‐word pairs in the semantically congruent condition. A greater late positive potential (LPP) (550–750 ms) was also elicited in the semantically incongruent safety sign‐word pairs, which was different from previous studies that observed larger LPP in congruent conditions. These results suggest complicated cognitive mechanisms of safety sign comprehension in construction workers. This study extends safety sign comprehension research by using electrophysiological approaches and provides useful indicators for researchers or safety engineers to measure the semantic congruity of proposed sign designs.

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“…Neuromanagement in engineering, which integrates research approaches in neuroscience into engineering management ( Yu et al, 2022 ), provide a powerful and advanced tool for human cognition and behavior research besides hazard recognition of construction workers. Emerging neuroscience techniques including eye-tracking, electroencephalogram (EEG) ( Cheng et al, 2022 ; Saedi et al, 2022 ), functional near-infrared spectroscopy (fNIRS) ( Zhou et al, 2021 ). EEG is a diagnostic imaging technique used to obtain information about brain activities by detecting the voltage variations induced by neurons on the scalp cortical surface ( Jeon and Cai, 2021 ; Ke et al, 2021 ; Peng et al, 2022 ).…”

Section: Introductionmentioning

confidence: 99%

A Systematic Review of Eye-Tracking Studies of Construction Safety

et al. 2022

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Safety is the most important concern in the construction industry, and construction workers’ attention allocation is closely associated with their hazard recognition and safety behaviors. The recent emergence of eye-tracking techniques allows researchers in construction safety to further investigate construction workers’ visual attention allocation during hazard recognition. The existing eye-tracking studies in construction safety need to be comprehensively understood, to provide practical suggestions for future research and on-site safety management. This study aims to summarize previous studies on the application of eye-tracking techniques to the construction safety context through a systematic literature review. The literature search and study selection process included 22 eligible studies. Content analysis was then carried out from participant selection, device selection, task design, area of interest determination, feature extraction, data analysis, and main findings. Major limitations of the existing studies are identified, and recommendations for future research in theoretical development, experiment improvement, and data analysis method advancement are proposed to address these limitations. Even though the application of eye-tracking techniques in construction safety research is still in its early stage, it is worth future continuous attention because relevant discoveries would be of great significance to hazard control and safety management in the construction industry.

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Applications of electroencephalography in construction

Cited by 40 publications

References 87 publications

Brain‐regulated learning for classifying on‐site hazards with small datasets

Brain‐regulated learning for classifying on‐site hazards with small datasets

Neural mechanisms behind semantic congruity of construction safety signs: An EEG investigation on construction workers

A Systematic Review of Eye-Tracking Studies of Construction Safety

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