Experimental Study on EEG with Different Cognitive Load

Liang, Yong; Liang, Wei; Qu, Jinping; Yang, Jie

doi:10.1109/smc.2018.00735

Cited by 6 publications

(5 citation statements)

References 6 publications

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“…Computer games [85], [103], [104] were included in the low-noise category. Data presentation tasks, visual [105]- [108] or auditory [109]- [111], and multiple learning tasks [13], [18], [112], [113], including code comprehension tasks [114]- [116] and mathematical tasks [117]- [120], were categorized as low-noise ones. Additionally, dual tasks with low noise were a Tetris game coupled with an oddball task [121], a Stroop task coupled with an oddball task [122], a SIMKAP task [123], and a threefold task performed on a computer and a customized AR system [14].…”

Section: A Rq (A): Classification Of Cognitive Load Experiments Based...mentioning

confidence: 99%

“…Exclusive standard preprocessing was frequently documented [13], [85], [90], [95], [98], [108], [109], [113], [118]- [120], [125], [129], [132], [134], [136]. Despite the advances in denoising techniques, standard methods still have their place in most preprocessing pipelines.…”

Section: ) How Was Standard Preprocessing Executed In Semi-automated ...mentioning

confidence: 99%

“…Regarding software toolboxes, the EEGLab package was preferred [90], [93], [99], [100], [104], [107], [108], [112], [114], [115], [117], [121]- [123], [125], [126], [128], [133], [135], [137], [138]. Additionally,ERPLab [122], [138],a customized Graphical User Interface (GUI) in MATLAB [113], MNE [14], [76], [102], BESA software [18], [140], ABM B-Alert Live software [124], and Fieldtrip were reported [106].…”

Section: ) Standardized Semi-automated Preprocessing Pipelinesmentioning

confidence: 99%

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A Comprehensive Survey of EEG Preprocessing Methods for Cognitive Load Assessment

Kyriaki,

Koukopoulos,

Fidas

2024

IEEE Access

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Preprocessing electroencephalographic (EEG) signals during computer-mediated Cognitive Load tasks is crucial in Human-Computer Interaction (HCI). This process significantly influences subsequent EEG analysis and the efficacy of Artificial Intelligence (AI) models employed in Cognitive Load Assessment.Consequently, it stands as an indispensable procedure for developing dependable systems capable of adapting to users' cognitive capacities and constraints. We systematically analyzed fifty-seven (57) research papers on computer-mediated Cognitive Load EEG experiments published between 2018 and 2023. The preprocessing methods identified were multiple, controversial, and strongly dependent on the particularities of each experiment and the derived experimental dataset. Our investigation involved the meticulous classification of preprocessing methods based on distinct parameters, namely the degree of user intervention, the noise level, and the subject pool size. Particular attention was paid to semi-automated denoising technology since conventional methods, advanced approaches, and standardized pipelines overwhelm research, but no optimum solution is available yet. This survey is anticipated to provide a valuable contribution to the rising demand for an efficient and fully automated preprocessing approach in EEG-based computerized Cognitive Load experiments.

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Section: A Rq (A): Classification Of Cognitive Load Experiments Based...mentioning

confidence: 99%

Section: ) How Was Standard Preprocessing Executed In Semi-automated ...mentioning

confidence: 99%

Section: ) Standardized Semi-automated Preprocessing Pipelinesmentioning

confidence: 99%

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A Comprehensive Survey of EEG Preprocessing Methods for Cognitive Load Assessment

Kyriaki,

Koukopoulos,

Fidas

2024

IEEE Access

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“…The intensity of brainwaves varies according to the state of human mental activity, and the degree of human cognitive load can be estimated based on this measurement. For example, alpha waves appear mainly in relaxation, beta waves appear in problem solving, and gamma waves appear mainly in more complex mental functions [ 25 ]. Considering these characteristics, we measured the constituent factors of cognitive load separately in terms of the degree of activation of the brainwaves by frequency.…”

Section: Related Workmentioning

confidence: 99%

Prediction of Cognitive Load from Electroencephalography Signals Using Long Short-Term Memory Network

2023

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In recent years, the development of adaptive models to tailor instructional content to learners by measuring their cognitive load has become a topic of active research. Brain fog, also known as confusion, is a common cause of poor performance, and real-time detection of confusion is a challenging and important task for applications in online education and driver fatigue detection. In this study, we propose a deep learning method for cognitive load recognition based on electroencephalography (EEG) signals using a long short-term memory network (LSTM) with an attention mechanism. We obtained EEG signal data from a database of brainwave information and associated data on mental load. We evaluated the performance of the proposed LSTM technique in comparison with random forest, Adaptive Boosting (AdaBoost), support vector machine, eXtreme Gradient Boosting (XGBoost), and artificial neural network models. The experimental results demonstrated that the proposed approach had the highest accuracy of 87.1% compared to those of other algorithms, including random forest (64%), AdaBoost (64.31%), support vector machine (60.9%), XGBoost (67.3%), and artificial neural network models (71.4%). The results of this study support the development of a personalized adaptive learning system designed to measure and actively respond to learners’ cognitive load in real time using wireless portable EEG systems.

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“…emotions or stress). Finally, physiological approaches are based on the evidence that changes in cognitive states are accompanied by physiological changes [25][26][27][28][29][30]. Their advantage is that they allow continuous recording of data and thus might be used for online adaptation of training environments.…”

Section: Introductionmentioning

confidence: 99%

Neural Correlates of Cognitive Load While Playing an Emergency Simulation Game: A Functional Near-Infrared Spectroscopy (fNIRS) Study

et al. 2022

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Functional near-infrared spectroscopy (fNIRS) provides reliable results for determining cognitive load based on averaged cortical blood flow during multiple repetitions of short cognitive tasks. At the same time, it remains unclear how to use this technique for assessing cognitive load during prolonged single-trial activity. In this study we used a computer-based emergency simulation game for inducing different levels of cognitive load. We propose a novel approach to measure cognitive load using specific time slots, determined based on simulation log-data interpreted in light of Barrouillet's timebased resource-sharing model. To validate this approach we compared cortical activity in DLPFC and left IFG regions measured at four specific time slots during a simulation. We found significant associations between cognitive load and neuronal activity within the DLPFC depending on the chosen time slot, whereas no such dependencies were found for the IFG. These results illustrate how knowledge of task structure could be used advantageously for the identification of cognitive load. Although requiring further investigation in terms of reliability and generalizability, the presented approach can be considered promising evidence that fNIRS might be suitable for more general reliable assessments of cognitive load during prolonged single-trial activities and for real-time adaptations in simulationbased learning environments.

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Experimental Study on EEG with Different Cognitive Load

Cited by 6 publications

References 6 publications

A Comprehensive Survey of EEG Preprocessing Methods for Cognitive Load Assessment

A Comprehensive Survey of EEG Preprocessing Methods for Cognitive Load Assessment

Prediction of Cognitive Load from Electroencephalography Signals Using Long Short-Term Memory Network

Neural Correlates of Cognitive Load While Playing an Emergency Simulation Game: A Functional Near-Infrared Spectroscopy (fNIRS) Study

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