EEG-based mental workload recognition related to multitasking

Lim, Wei Lun; Sourina, Olga; Liu, Yisi; Wang, Lipo

doi:10.1109/icics.2015.7459834

Cited by 30 publications

(19 citation statements)

References 20 publications

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“…Some advantages of using the Emotiv EPOC is its low cost, good signal-to-noise ratio, and ease of use (Duvinage et al, 2013). In addition, the EPOC has shown satisfactory results in diverse research studies in emotion recognition (Ramirez and Vamvakousis, 2012), brain computer interface (Holewa and Nawrocka, 2014), and cognitive workload (Lim et al, 2015). correspond to: AF3, F7, F3, FC5, T7, P7, O1, O2, P8, T8, FC6, F4, F8, AF4, M1, and M2.…”

Section: Electroencephalographic (Eeg) Measurementmentioning

confidence: 99%

Electroencephalographic Workload Indicators During Teleoperation of an Unmanned Aerial Vehicle Shepherding a Swarm of Unmanned Ground Vehicles in Contested Environments

et al. 2020

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Background: Although many electroencephalographic (EEG) indicators have been proposed in the literature, it is unclear which of the power bands and various indices are best as indicators of mental workload. Spectral powers (Theta, Alpha, and Beta) and ratios (Beta/(Alpha + Theta), Theta/Alpha, Theta/Beta) were identified in the literature as prominent indicators of cognitive workload. Objective: The aim of the present study is to identify a set of EEG indicators that can be used for the objective assessment of cognitive workload in a multitasking setting and as a foundational step toward a human-autonomy augmented cognition system. Methods: The participants' perceived workload was modulated during a teleoperation task involving an unmanned aerial vehicle (UAV) shepherding a swarm of unmanned ground vehicles (UGVs). Three sources of data were recorded from sixteen participants (n = 16): heart rate (HR), EEG, and subjective indicators of the perceived workload using the Air Traffic Workload Input Technique (ATWIT). Results: The HR data predicted the scores from ATWIT. Nineteen common EEG features offered a discriminatory power of the four workload setups with high classification accuracy (82.23%), exhibiting a higher sensitivity than ATWIT and HR. Conclusion: The identified set of features represents EEG indicators for the objective assessment of cognitive workload across subjects. These common indicators could be used for augmented intelligence in human-autonomy teaming scenarios, and form the basis for our work on designing a closed-loop augmented cognition system for human-swarm teaming.

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Section: Electroencephalographic (Eeg) Measurementmentioning

confidence: 99%

Electroencephalographic Workload Indicators During Teleoperation of an Unmanned Aerial Vehicle Shepherding a Swarm of Unmanned Ground Vehicles in Contested Environments

et al. 2020

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“…For workload, the Stroop Color Test is used to evoke different levels of workload. More details about the calibration and the EEG-based brain states recognition algorithms can be found in our previous work [18] and [19]. After calibration, the ACTOs kept wearing the device and had their EEG recorded throughout the whole experiment.…”

Section: Eeg Recordingmentioning

confidence: 99%

EEG-Based Human Factors Evaluation of Air Traffic Control Operators (ATCOs) for Optimal Training

Liu

Lan

Traspsilawati

et al. 2019

2019 International Conference on Cyberworlds (CW)

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To deal with the increasing demands in Air Traffic Control (ATC), new working place designs are proposed and developed that need novel human factors evaluation tools. In this paper, we propose a novel application of Electroencephalogram (EEG)-based emotion, workload, and stress recognition algorithms to investigate the optimal length of training for Air Traffic Control Officers (ATCOs) to learn working with threedimensional (3D) display as a supplementary to the existing 2D display. We tested and applied the state-of-the-art EEG-based subject-dependent algorithms. The following experiment was carried out. Twelve ATCOs were recruited to take part in the experiment. The participants were in charge of the Terminal Control Area, providing navigation assistance to aircraft departing and approaching the airport using 2D and 3D displays. EEG data were recorded, and traditional human factors questionnaires were given to the participants after 15-minute, 60minute, and 120-minute training. Different from the questionnaires, the EEG-based evaluation tools allow the recognition of emotions, workload, and stress with different temporal resolutions during the task performance by subjects. The results showed that 50-minute training could be enough for the ATCOs to learn the new display setting as they had relatively low stress and workload. The study demonstrated that there is a potential of applying the EEG-based human factors evaluation tools to assess novel system designs in addition to traditional questionnaire and feedback, which can be beneficial for future improvements and developments of the systems and interfaces.

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“…At present, it is very interesting to employ the linear or non-linear methods to classify EEG signals in different states. Some researchers used neural network methods to classify EEG in different states [29][30][31][32][33], such as emotion recognition [30], fatigue detection [31], epilepsy prediction [32,33], and some other diseases. Furthermore, other researchers achieved the purpose of state classification by calculating the complexity of EEG signals [12,34].…”

Section: Introductionmentioning

confidence: 99%

A Modified Multivariable Complexity Measure Algorithm and Its Application for Identifying Mental Arithmetic Task

Ma¹,

He²,

Sun³

2021

Entropy

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Properly measuring the complexity of time series is an important issue. The permutation entropy (PE) is a widely used as an effective complexity measurement algorithm, but it is not suitable for the complexity description of multi-dimensional data. In this paper, in order to better measure the complexity of multi-dimensional time series, we proposed a modified multivariable PE (MMPE) algorithm with principal component analysis (PCA) dimensionality reduction, which is a new multi-dimensional time series complexity measurement algorithm. The analysis results of different chaotic systems verify that MMPE is effective. Moreover, we applied it to the comlexity analysis of EEG data. It shows that the person during mental arithmetic task has higher complexity comparing with the state before mental arithmetic task. In addition, we also discussed the necessity of the PCA dimensionality reduction.

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EEG-based mental workload recognition related to multitasking

Cited by 30 publications

References 20 publications

Electroencephalographic Workload Indicators During Teleoperation of an Unmanned Aerial Vehicle Shepherding a Swarm of Unmanned Ground Vehicles in Contested Environments

Electroencephalographic Workload Indicators During Teleoperation of an Unmanned Aerial Vehicle Shepherding a Swarm of Unmanned Ground Vehicles in Contested Environments

EEG-Based Human Factors Evaluation of Air Traffic Control Operators (ATCOs) for Optimal Training

A Modified Multivariable Complexity Measure Algorithm and Its Application for Identifying Mental Arithmetic Task

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