An EEG-Based Cognitive Load Assessment in Multimedia Learning Using Feature Extraction and Partial Directed Coherence

Mazher, Moona; Aziz, Azrina Abd; Malik, Aamir Saeed; Amin, Hafeez Ullah

doi:10.1109/access.2017.2731784

Cited by 63 publications

(29 citation statements)

References 52 publications

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“…For instance, increase in theta band power over the frontal cortex has been associated with an increase in task difficulty and use of more working memory resources (Parasuraman and Caggiano, 2002). Suppression of Alpha power has been observed in the parietal and occipital areas during increase of mental workload (Mazher et al, 2017;Puma et al, 2018). Increase in Beta power over the parietal and occipital cortical regions has been observed during visual working memory tasks (Mapelli and Özkurt, 2019).…”

Section: Discussionmentioning

confidence: 99%

“…An increase in alpha power is related to lower mental vigilance and alertness (MacLean et al, 2012;Kamzanova et al, 2014) and therefore a decrease in the attention resources allocated to the task (Vidulich and Tsang, 2012). On the other hand, a progressive suppression of alpha waves has been linked to increasing levels of task difficulty (Mazher et al, 2017). Cortical areas that have been associated with alpha band changes are parietal and occipital areas (Dasari et al, 2017;Puma et al, 2018).…”

Section: Eeg Indicators Of Mental Workloadmentioning

confidence: 99%

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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: Discussionmentioning

confidence: 99%

Section: Eeg Indicators Of Mental Workloadmentioning

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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“…Spectral entropy (SE) is used to determine the randomness of energy distribution of the signal. A higher SE indicates signal energy is less concentrated to specific region on the time-frequency plane [9,23]. SE can be expressed as…”

Section: Spectral Entropymentioning

confidence: 99%

A New Competitive Binary Grey Wolf Optimizer to Solve the Feature Selection Problem in EMG Signals Classification

et al. 2018

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Features extracted from the electromyography (EMG) signal normally consist of irrelevant and redundant features. Conventionally, feature selection is an effective way to evaluate the most informative features, which contributes to performance enhancement and feature reduction. Therefore, this article proposes a new competitive binary grey wolf optimizer (CBGWO) to solve the feature selection problem in EMG signals classification. Initially, short-time Fourier transform (STFT) transforms the EMG signal into time-frequency representation. Ten time-frequency features are extracted from the STFT coefficient. Then, the proposed method is used to evaluate the optimal feature subset from the original feature set. To evaluate the effectiveness of proposed method, CBGWO is compared with binary grey wolf optimization (BGWO1 and BGWO2), binary particle swarm optimization (BPSO), and genetic algorithm (GA). The experimental results show the superiority of CBGWO not only in classification performance, but also feature reduction. In addition, CBGWO has a very low computational cost, which is more suitable for real world application.

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“…Dan and Reiner [32] examined the cognitive load of the participants whom connected to EEG system, on the learning of origami, which includes simple and complex tasks in 2D and 3D screens. Mazher, Abd Aziz, Malik and Ullah Amin [33] measured the cognitive load using EEG to understand the complexity of the multimedia learning task in their article. Zhang, Zhao, Bai and Tian [34] examined the functional connection in the brain when the working memory reached its capacity when participants performed a visual working memory with increasing loads from 1 to 6.…”

Section: Occipital Lobementioning

confidence: 99%

Usage of Augmented Reality Glasses in Automotive Industry: Age-Related Effects on Cognitive Load

IKIZ*¹,

ATICI-ULUSU²,

TASKAPILIOGLU³

et al. 2019

IJRTE

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With the development of technology, augmented reality (AR) devices were introduced in many areas. One of these devices, AR glasses, is more convenient and easy to fulfil and still continuing to evolve. The AR glasses, which are frequently used in sectors such as education, entertainment, construction and automotive, bring many research areas. In this article, the cognitive load that AR glasses bring to the users who is working on an assembly line has been investigated. Cognitive load refers to the resources used by working memory in the brain. This study was carried out with healthy participants in the assembly line of an automotive manufacturing factory and 60 tests were performed for measurement. Effect of the AR glasses for participants under 35 and over 35 years of age was measured by electroencephalography (EEG). EEG is a common objective technique used in cognitive load measurements. EEG data collected were examined and no significant difference was observed between participants of under and over 35 years of age. When compared with the data obtained in the experiments without glasses, it was seen that the use of the AR glasses in assembly lines of automotive manufacturing factories did not create an additional cognitive load. It can be seen from the article that AR glasses did not have an age-related effect and can be use in the automotive manufacturing industry

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An EEG-Based Cognitive Load Assessment in Multimedia Learning Using Feature Extraction and Partial Directed Coherence

Cited by 63 publications

References 52 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

A New Competitive Binary Grey Wolf Optimizer to Solve the Feature Selection Problem in EMG Signals Classification

Usage of Augmented Reality Glasses in Automotive Industry: Age-Related Effects on Cognitive Load

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