Using Wireless EEG Signals to Assess Memory Workload in the &lt;inline-formula&gt; &lt;tex-math notation="LaTeX"&gt; $n$&lt;/tex-math&gt; &lt;/inline-formula&gt;-Back Task

Wang, Shouyi; Gwizdka, Jacek; Chaovalitwongse, Wanpracha Art

doi:10.1109/thms.2015.2476818

Cited by 148 publications

(46 citation statements)

References 44 publications

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“…Objective measures are generally based on experimental methods used to collect physiological and/or behavioral information by a single sensor or a combination of different types of sensors, simultaneously (Debie et al, 2019). In contrast with subjective measures, objective techniques offer a continuous measure of workload in real time, and also their implementations do not interfere with the performance of the task at hand (Wang et al, 2015). In general, objective measures can be classified either as neurophysiological, physiological, or behavioral.…”

Section: Introductionmentioning

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: Introductionmentioning

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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“…uncertainty modeling, emotional state classification) [10] [11], performance assessment (e.g. outcome prediction, learners' classification) [12] [13] [14] [15] [16] and users' mental activity assessment [17] [18] [19].…”

Section: Introductionmentioning

confidence: 99%

Tracking Students’ Mental Engagement Using EEG Signals during an Interaction with a Virtual Learning Environment

Khedher¹,

Jraidi²,

Frasson³

2019

JILSA

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Monitoring students' level of engagement during learning activities is an important challenge in the development of tutoring interventions. In this paper, we explore the feasibility of using electroencephalographic signals (EEG) as a tool to monitor the mental engagement index of novice medicine students during a reasoning process. More precisely, the objectives were first, to track students' mental engagement evolution in order to investigate whether there were particular sections within the learning environment that aroused the highest engagement level among the students, and, if so, did these sections have an impact on learners' performance. Experimental analyses showed the same trends in the different resolution phases as well as across the different regions of the environments. However, we noticed a higher engagement index during the treatment identification phase since it aroused more mental effort. Moreover statistically significant effects were found between mental engagement and students' performance.

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“…Each individual segment is supposed to be sampled from the same data population. In this way, it is possible to capture the repeatable and reproducible causal relations underlying the EEG data collected from loosely 15/47 (Blanco et al, 2017), (Bono et al, 2016), (Brown, Grundlehner, & Penders, 2011), (Duan, Zhu, & Lu, 2013), (Frantzidis et al, 2010), (Liu, Sourina, & Nguyen, 2010), (Petrantonakis & Hadjileontiadis, 2010), (Yohanes et al, 2012), (Singh, Singh, & Sandel, 2014), (Wang, Nie, & Lu, 2014), (Rozgić, Vitaladevuni, & Prasad, 2013), (Al-shargie et al, 2016) (Sharma & Gomes, 2015), (Shi, Jiao, & Lu, 2013), (Wang & Sourina, 2013), (Wang, Gwizdka, & Chaovalitwongse, 2016) K Nearest Neighbors (KNN) Frequency domain, time-frequency domain (Blanco et al, 2017), (Brown et al, 2011), (Duan et al, 2013), (Hadjidimitriou, Charisis, & Hadjileontiadis, 2015), (Khosrowabadi et al, 2010), (Murugappan et al, 2010), (Petrantonakis Time domain, frequency domain (Kroupi, Yazdani, & Ebrahimi, 2011), (Nielsen & Chénier, 1999), (Reuderink, Mühl, & Poel, 2013) (Hanouneh et al, 2016) Microstate analysis Time domain (Nguyen, Nguyen, & Zeng, 2015), (Faber et al, 2017), (Nguyen, Nguyen, & Zeng, 2019) Quadratic discriminant analysis (QDA) Frequency domain,…”

Section: Clustering-based Segmentation Of Unstructured Design Protocolmentioning

confidence: 99%

A tEEG framework for studying designer’s cognitive and affective states

et al. 2020

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This paper proposes a task-related electroencephalogram research framework (tEEG framework) to guide scholars’ research on EEG-based cognitive and affective studies in the context of design. The proposed tEEG framework aims to investigate design activities with loosely controlled experiments and decompose a complex design process into multiple primitive cognitive activities, corresponding to which different research hypotheses on basic design activities can be effectively formulated and tested. Thereafter, existing EEG techniques and methods can be applied to analyse EEG signals related to design. Three application examples are presented at the end of this paper to demonstrate how the proposed framework can be applied to analyse design activities. The tEEG framework is presented to guide EEG-based cognitive and affective studies in the context of design. Existing methods and models are summarized, for the effective application of the tEEG framework, from the current literature spread in a wide spectrum of resources and fields.

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Using Wireless EEG Signals to Assess Memory Workload in the <inline-formula> <tex-math notation="LaTeX"> $n$</tex-math> </inline-formula>-Back Task

Cited by 148 publications

References 44 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

Tracking Students’ Mental Engagement Using EEG Signals during an Interaction with a Virtual Learning Environment

A tEEG framework for studying designer’s cognitive and affective states

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