EEG-based Cognitive Load assessment in Matlab GUI and impact on Learning System

Ahmed, Sabbir; Walid, Md. Abul Ala; Islam, Mohammad Mahfuzul

doi:10.1109/icaict51780.2020.9333485

Cited by 6 publications

(8 citation statements)

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“…As a result, the type of interface used when teaching programming can influence the development of computational thinking skills. [35]: This research focuses on reducing the cognitive load for computational thinkingbased learning. The aim is to build a graphical user interface for MATLAB software in order to instantly measure cognitive load based on the electroencephalograms of individuals.…”

Section: Resultsmentioning

confidence: 99%

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Computational Thinking and User Interfaces: A Systematic Review

2022

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This document presents a systematic bibliographic review that demonstrates the need to conduct research on how the user experience impacts the development of computational thinking.Background: In the field of computer science, computational thinking is defined as a method that enhances problem-solving skills, system design, and human behavior understanding. Over the last few decades, several tools have been proposed for the development of computational thinking skills; however, there is no area of study that evaluates the implications or the impact that these types of platforms have on users belonging to any knowledge area.Research Question: Do user interfaces influence the development of computational thinking skills?Methodology: To address this issue, a systematic review of the literature was conducted using the preferred reporting items for systematic reviews and meta-analyses (PRISMA) methodology for analyzing and evaluating scientific publications.Findings: The results show that despite the dearth of literature on the subject, the specific design of a user interface has a significant impact on the development of computational thinking. Bearing the above in mind, it is necessary to conduct research that delves more deeply into the effects caused by the technologies that are used to develop computational thinking, this being a line of research that is worthy of consideration.

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Section: Resultsmentioning

confidence: 99%

“…It is also important to consider the cognitive load imposed by these tools, since the user learning process could be frustrated, resulting in a negative experience and a counterproductive outcome [14], [15], [23], [33], [35].…”

Section: Discussionmentioning

confidence: 99%

Computational Thinking and User Interfaces: A Systematic Review

2022

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“…However, this method requires participants to compare many relevant factors, easily making participants feel fatigued and inaccurate experimental data (Piwowarski, Singh, & Nermend, 2020). To address this issue, EEG-based assessments were adopted by (Ahmed, Walid, & Islam, 2020) to measure the mental load under the CT learning environment. More specifically, Ahmed's study implements MATLAB GUI to immediately calculate the theta/beta ratio to differentiate the level of mental load usage of CT and non-CT groups in real-time.…”

Section: Electroencephalogram (Eeg)mentioning

confidence: 99%

Sports digitalization – realizing the potential value of tracking technologies in professional sports organizations

Konzag,

Sølvkær Schütz

2024

Proceedings of the Annual Hawaii International Conference on System Sciences

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Human-robot collaboration (HRC) has become an emerging field, where the use of a robotic agent has been shifted from a supportive machine to a decisionmaking collaborator. A variety of factors can influence the effectiveness of decision-making processes during HRC, including the system-related (e.g., robot capability) and human-related (e.g., individual knowledgeability) factors. As a variety of contextual factors can significantly impact the human-robot decision-making process in collaborative contexts, the present study adopts a Lego-like EEG headset to collect and examine human brain activities and utilizes multiple questionnaires to evaluate participants' cognitive perceptions toward the robot. A user study was conducted where two levels of robot capabilities (high vs. low) were manipulated to provide system recommendations. The participants were also identified into two groups based on their computational thinking (CT) ability. The EEG results revealed that different levels of CT abilities trigger different brainwaves, and the participants' trust calibration of the robot also varies the resultant brain activities.

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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%

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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EEG-based Cognitive Load assessment in Matlab GUI and impact on Learning System

Cited by 6 publications

References 10 publications

Computational Thinking and User Interfaces: A Systematic Review

Computational Thinking and User Interfaces: A Systematic Review

Sports digitalization – realizing the potential value of tracking technologies in professional sports organizations

A Comprehensive Survey of EEG Preprocessing Methods for Cognitive Load Assessment

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