Biosignals in Human Factors Research for Heavy Equipment Operators: A Review of Available Methods and Their Feasibility in Laboratory and Ambulatory Studies

Hekmatmanesh, Amin; Zhidchenko, Victor; Kauranen, Kari; Siitonen, Kaija; Handroos, Heikki; Soutukorva, Sari; Kilpeläinen, Asko

doi:10.1109/access.2021.3092516

Cited by 17 publications

(14 citation statements)

References 71 publications

(140 reference statements)

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“…One of the reasons may be that none of the participants had previous experience in controlling a real experimental vehicle via the BCI before the experiment. In addition, other uncontrollable factors still exist, such as light intensity, noise and the smoothness of the road, which have impacts on the performance of SSVEP-based BCIs in outdoor driving environments [41][42][43]. More practice should facilitate improvement in the performance of controlling BCVs.…”

Section: Discussionmentioning

confidence: 99%

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A Brain-Controlled Vehicle System Based on Steady State Visual Evoked Potentials

Zhang

Han

et al. 2022

Cogn Comput

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In this paper, we propose a human-vehicle cooperative driving system. The objectives of this research are twofold: (1) providing a feasible brain-controlled vehicle (BCV) mode; (2) providing a human-vehicle cooperative control mode. For the first aim, through a brain-computer interface (BCI), we can analyse the EEG signal and get the driving intentions of the driver. For the second aim, the human-vehicle cooperative control is manifested in the BCV combined with the obstacle detection assistance. Considering the potential dangers of driving a real motor vehicle in the outdoor, an obstacle detection module is essential in the human-vehicle cooperative driving system. Obstacle detection and emergency braking can ensure the safety of the driver and the vehicle during driving. EEG system based on steady-state visual evoked potential (SSVEP) is used in the BCI. Simulation and real vehicle driving experiment platform are designed to verify the feasibility of the proposed human-vehicle cooperative driving system. Five subjects participated in the simulation experiment and real the vehicle driving experiment. The outdoor experimental results show that the average accuracy of intention recognition is 90.68 ± 2.96% on the real vehicle platform. In this paper, we verified the feasibility of the SSVEP-based BCV mode and realised the human-vehicle cooperative driving system.

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Section: Discussionmentioning

confidence: 99%

“…One limitation of the proposed framework is that stress and noise are the main factors that affect the accuracy level and reliability of the BCV [43]. Another limitation is that the vehicle control commands converted by EEG signals are switching values in the form of 0's and 1's, which cannot perform fine tuning, such as steering.…”

Section: Discussionmentioning

confidence: 99%

A Brain-Controlled Vehicle System Based on Steady State Visual Evoked Potentials

Zhang

Han

et al. 2022

Cogn Comput

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“…Stress assessment by EEG takes advantage of the higher temporal resolution and the possibility of direct access to the activity of the central nervous system, measuring stress levels continuously and without interfering with human activities (Borghini et al, 2020 ). Moreover, there are several examples of real-time measures of stress based on EEG signals using machine learning methods (Saeed et al, 2015 ; Attallah, 2020 ; Halim and Rehan, 2020 ; Hekmatmanesh et al, 2021 ; Katmah et al, 2021 ).…”

Section: Introductionmentioning

confidence: 99%

Evaluation of a New Lightweight EEG Technology for Translational Applications of Passive Brain-Computer Interfaces

Sciaraffa¹,

Flumeri

Germano³

et al. 2022

Front. Hum. Neurosci.

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Technologies like passive brain-computer interfaces (BCI) can enhance human-machine interaction. Anyhow, there are still shortcomings in terms of easiness of use, reliability, and generalizability that prevent passive-BCI from entering real-life situations. The current work aimed to technologically and methodologically design a new gel-free passive-BCI system for out-of-the-lab employment. The choice of the water-based electrodes and the design of a new lightweight headset met the need for easy-to-wear, comfortable, and highly acceptable technology. The proposed system showed high reliability in both laboratory and realistic settings, performing not significantly different from the gold standard based on gel electrodes. In both cases, the proposed system allowed effective discrimination (AUC > 0.9) between low and high levels of workload, vigilance, and stress even for high temporal resolution (<10 s). Finally, the generalizability of the proposed system has been tested through a cross-task calibration. The system calibrated with the data recorded during the laboratory tasks was able to discriminate the targeted human factors during the realistic task reaching AUC values higher than 0.8 at 40 s of temporal resolution in case of vigilance and workload, and 20 s of temporal resolution for the stress monitoring. These results pave the way for ecologic use of the system, where calibration data of the realistic task are difficult to obtain.

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“…Thus, the major problem in the human-machine system is being able to measure MW levels. It is important to research the control of MW, fatigue and stress levels for busy human-machine interface operators [3][4][5].…”

Section: Introductionmentioning

confidence: 99%

Sensitive Channel Selection for Mental Workload Classification

Jin

Qu²,

Pang³

et al. 2022

Mathematics

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Mental workload (MW) assessment has been widely studied in various human–machine interaction tasks. The existing researches on MW classification mostly use non-invasive electroencephalography (EEG) caps to collect EEG signals and identify MW levels. However, the activation region of the brain stimulated by MW tasks is not the same for every subject. It may be inappropriate to use EEG signals from all electrode channels to identify MW. In this paper, an EEG rhythm energy heatmap is first established to visually show the change trends in the energy of four EEG rhythms with time, EEG channels and MW levels. It can be concluded from the presented heatmaps that this change trend varies with subjects, rhythms and channels. Based on the analysis, a double threshold method is proposed to select sensitive channels for MW assessment. The EEG signals of personalized selected channels, named positive sensitive channels (PSCs) and negative sensitive channels (NSCs), are used for MW classification using the Support Vector Machine (SVM) algorithm. The results show that the selection of personalized sensitive channels generally contributes to improving the performance of MW classification.

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Biosignals in Human Factors Research for Heavy Equipment Operators: A Review of Available Methods and Their Feasibility in Laboratory and Ambulatory Studies

Cited by 17 publications

References 71 publications

A Brain-Controlled Vehicle System Based on Steady State Visual Evoked Potentials

A Brain-Controlled Vehicle System Based on Steady State Visual Evoked Potentials

Evaluation of a New Lightweight EEG Technology for Translational Applications of Passive Brain-Computer Interfaces

Sensitive Channel Selection for Mental Workload Classification

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