Studying the generalisability of cognitive load measured with EEG

Gómez, Luis Cabañero; Hervás, Ramón; Gónzalez, Iván; Villarreal, Vladimir

doi:10.1016/j.bspc.2021.103032

Cited by 12 publications

(6 citation statements)

References 38 publications

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“…Consequently, undertaking a quantitative comparison of the reported accuracies proves unfeasible. In qualitative terms, it is discernible that our proposed methodology surpasses the performance of [15], [18], and [19], albeit falling short in comparison to the methodologies described in [21]- [24]. This discrepancy in performance arises from the fact that our current study employs distinct sets of EEG sources from different tasks for each individual, whereas the aforementioned studies [21]- [24] employ consistent individual sets.…”

Section: Comparison With Reported Studiesmentioning

confidence: 69%

“…The results derived by a singlehidden layer feedforward neural network demonstrated an average classification accuracy of 44.8% for binary workload levels across the three tasks. Gómez et al [19] assembled 19 subjects and collected EEG recordings during tasks involving the N-back and Stroop [20] paradigms. They accomplished a cross-task recognition accuracy of 55.64% for binary workload levels, utilizing a RF classifier.…”

Section: Related Workmentioning

confidence: 99%

“…Passive brain-computer interfaces (BCIs) and machine learning approaches have been employed to acquire and decode EEG signals for evaluating cognitive workload in previous works [5]- [8]. In this context, the common approach for EEG decoding involves decomposing the signals into delta (1-4 Hz), theta (4-8 Hz), alpha (8)(9)(10)(11)(12), beta (12)(13)(14)(15)(16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30), and gamma (>30 Hz) frequency bands, allowing individual examination of the power spectral density (PSD) in each band [9]. Among the different PSD bands, previous studies have demonstrated that theta and alpha rhythms are closely associated with cognitive and memory processes [9].…”

Section: Introductionmentioning

confidence: 99%

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Medicine-Engineering Interdisciplinary Research Based on Bibliometric Analysis: A Case Study on Medicine-Engineering Institutional Cooperation of Shanghai Jiao Tong University

Wang

Cui

Deng

2022

J. Shanghai Jiaotong Univ. (Sci.)

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This article aims to provide reference for medicine-engineering interdisciplinary research. Targeted at the scientific literature and patent literature published by Shanghai Jiao Tong University, this article attempts to set up co-occurrence matrix of medicine-engineering institutional information which was extracted from address fields of the papers, so as to construct the medicine-engineering intersection datasets. The dataset of scientific literature was analyzed using bibliometrics and visualization methods from multiple dimensions, and the most active factors, such as trends of output, journal and subject distribution, were identified from the indicators of category normalized citation impact (CNCI), times cited, keywords, citation topics and the degree of medicine-engineering interdisplinary. Research on hotspots and trends was discussed in detail. Analyses of the dataset of patent literature showed research themes and measured the degree for technology convergence of medicine-engineering.

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Section: Comparison With Reported Studiesmentioning

confidence: 69%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Medicine-Engineering Interdisciplinary Research Based on Bibliometric Analysis: A Case Study on Medicine-Engineering Institutional Cooperation of Shanghai Jiao Tong University

Wang

Cui

Deng

2022

J. Shanghai Jiaotong Univ. (Sci.)

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“…The hjorth parameters are all statistical values derived using the variance. Hjorth activity is given by calculating varience of the channel [27]. Hjorth mobility is given by…”

Section: It Represents the Position Or Shift Of The Filter Coefficien...mentioning

confidence: 99%

MINDSCOPE: Machine-learning Inferencing of NeuroData for Seamless Cognitive Overload Prediction and Evaluation

Katinni,

Bablani

2024

Preprint

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Passive brain-computer interfaces (P-BCIs) offer the ability to dynamically adapt system interactions by inferring users’ cognitive states, such as levels of mental workload, from neural data. The P-BCI devices have many applications from analyzing and designing educational materials to monitoring the mental health of professionals operating airplanes, nuclear power plants, and airline traffic dispatchers. Electroencephalography (EEG) is the most suitable P-BCI device that can easily find applications in daily life due to its cost efficiency and device setup simplicity as compared to other devices like Functional near- infrared spectroscopy (fNIRS) or Functional Magnetic Resonance Imaging (fMRI) that are either costly or are bulky equipment that cannot be used in real life scenarios. The goal of this work is to classify the cognitive load of the user using his/her EEG output using machine learning (ML) techniques, following the MINDSCOPE (Machine Learning Inferencing of NeuroData for Seamless Cognitive Overload Prediction and Evaluation) methodology. The data acquisition was done by recording the EEG signal of the users performing n-back tasks of two different load intensities (rest and 2-back). This study accurately classified user cognitive load as high or low, allowing for comparison of several ML models and their performance in cognitive load discrimination. Feature subset analysis identified the combina- tion with the highest classification accuracy, shedding light on effective feature characterization for cognitive load assessment. Depending on the context of P-BCI use, the current work gives a starting point for choosing appropriate ML paradigms and feature characterizations to incorporate for a specific cognitive assessment task.

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“…Posts with low popularity attract fewer viewers and less attention, so the herding effect means that other users will also reduce their attention to the post ( Mattke et al, 2020 ). Since processing capacity is limited, individuals may prefer to allocate it to simple cognitive tasks ( Cabañero Gómez et al, 2021 ). The implication is that viewers are more willing to process straightforward advertisements than metaphorical advertisements in low popularity posts.…”

Section: Literature Review and Hypothesis Developmentmentioning

confidence: 99%

Metaphorical or Straightforward? Comparing the Effectiveness of Different Types of Social Media Advertising

et al. 2022

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The existing studies have analyzed the advertising effects of metaphorical advertisements and straightforward adverts in traditional advertising media. However, their advertising effects on social media are still unclear. To address this issue, this study uses eye-tracking and questionnaires to examine two types of social media tourism advertising—metaphorical and straightforward in posts with both high and low popularity. This within-subject (n = 55) experiment was designed to examine the effects of social media tourism advertising types on visual attention and tourism intention and to identify the moderating role of post popularity based on the elaboration likelihood model (ELM). We found that advertising types had no significant effect on visual attention, but metaphorical advertisements increased tourism intention compared with straightforward adverts. Furthermore, we found that the level of post popularity moderated the effect of advertising types on visual attention. Specifically, metaphorical adverts in highly popular posts attracted more visual attention in the advertising text area and in the whole advert. Straightforward adverts in posts with low popularity attracted more visual attention in the advertising text area. This research advances the current literature by exploring the effects of social media tourism advertising types and has implications for managers deciding on strategies for social media tourism marketing.

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Studying the generalisability of cognitive load measured with EEG

Cited by 12 publications

References 38 publications

Medicine-Engineering Interdisciplinary Research Based on Bibliometric Analysis: A Case Study on Medicine-Engineering Institutional Cooperation of Shanghai Jiao Tong University

Medicine-Engineering Interdisciplinary Research Based on Bibliometric Analysis: A Case Study on Medicine-Engineering Institutional Cooperation of Shanghai Jiao Tong University

MINDSCOPE: Machine-learning Inferencing of NeuroData for Seamless Cognitive Overload Prediction and Evaluation

Metaphorical or Straightforward? Comparing the Effectiveness of Different Types of Social Media Advertising

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