Work in Progress: Using Neuro-responses to Understand Creativity, the Engineering Design Process, and Concept Generation

Hartog, Tess; Marshall, Megan; Alhashim, Amin G.; Ahad, Tanvir; Siddique, Zahed

doi:10.18260/1-2--35701

Cited by 3 publications

(3 citation statements)

References 36 publications

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“…The common condition had the least negative mean amplitude of 0.128μV, the mean amplitude for the creative condition was -0.677μV, and the nonsense condition had the greatest negative mean amplitude at -1.137μV. Although the differences in the means for all pairs were insignificant (common-creative: p=1, d=.32; common-nonsense: p=1, d=.42; creative-nonsense: p=1, d=.16), this follows the trends in previous studies [12], [13], [21], [22]. A linear trend in the modulation of the N400 for the factor condition was reported in the Rutter et al 2012 [12] study, but no significant linear trend (F (1,3) = 1.28; p = .339; ηp 2 = .3) for condition was observed in this study.…”

Section: The N400 Componentsupporting

confidence: 86%

“…As observed in previous studies, the signal displays a negative dip for creative and nonsense conditions in the N400 (300-500ms) time window, with similar amplitudes at their negative peaks. [12], [13], [21], [22]. The repeated measures ANOVA revealed significant main effects for the factor electrode (F(2,6) = 11.79; p = .033; ηp 2 = .797; ωp 2 = .09), however main effects were not significant for the factor condition (F(2,6) = .392; p = .663; ηp 2 = .116; ωp 2 = -.06) or the condition-electrode interaction (F(4,12) = 1.614; p = .234; ηp 2 = .35; ωp 2 = .00).…”

Section: Resultsmentioning

confidence: 99%

See 1 more Smart Citation

Event Related Potentials (ERP) Study to Understand Function to Object Mapping for Engineering Student

Siddique,

Ahad,

Mobaraki-Omoumi

et al.

2022 ASEE Annual Conference &Amp; Exposition Proceedings

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Section: The N400 Componentsupporting

confidence: 86%

Section: Resultsmentioning

confidence: 99%

Event Related Potentials (ERP) Study to Understand Function to Object Mapping for Engineering Student

Siddique,

Ahad,

Mobaraki-Omoumi

et al.

2022 ASEE Annual Conference &Amp; Exposition Proceedings

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“…A significant amount of studies have been conducted from different perspectives in healthcare sectors [72][73][74]. Industry 4.0 enables technologies to make the healthcare sector more straightforward than before; for example, improving and enabling remote monitoring systems based on IoT.…”

Section: Industry 40 For Medical/healthcare Servicesmentioning

confidence: 99%

Industry 4.0 Applications for Medical/Healthcare Services

Paul

Riffat

Yasir

et al. 2021

JSAN

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At present, the whole world is transitioning to the fourth industrial revolution, or Industry 4.0, representing the transition to digital, fully automated environments, and cyber-physical systems. Industry 4.0 comprises many different technologies and innovations, which are being implemented in many different sectors. In this review, we focus on the healthcare or medical domain, where healthcare is being revolutionized. The whole ecosystem is moving towards Healthcare 4.0, through the application of Industry 4.0 methodologies. Many technical and innovative approaches have had an impact on moving the sector towards the 4.0 paradigm. We focus on such technologies, including Internet of Things, Big Data Analytics, blockchain, Cloud Computing, and Artificial Intelligence, implemented in Healthcare 4.0. In this review, we analyze and identify how their applications function, the currently available state-of-the-art technologies, solutions to current challenges, and innovative start-ups that have impacted healthcare, with regards to the Industry 4.0 paradigm.

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Electroencephalogram Experimentation to Understand Creativity of Mechanical Engineering Students

Ahad

Hartog

Alhashim

et al. 2023

ASME Open Journal of Engineering

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Electroencephalogram (EEG) alpha power (8–13 Hz) is a characteristic of various creative task conditions and is involved in creative ideation. Alpha power varies as a function of creativity-related task demands. This study investigated the event-related potentials (ERPs), alpha power activation, and potential machine learning (ML) to classify the neural responses of engineering students involved with creativity task. All participants performed a modified alternate uses task (AUT), in which participants categorized functions (or uses) for everyday objects as either creative, nonsense, or common. At first, this study investigated the fundamental ERPs over central and parietooccipital temporal areas. The bio-responses to understand creativity in engineering students demonstrates that nonsensical and creative stimuli elicit larger N400 amplitudes (−1.107 mV and −0.755 mV, respectively) than common uses (0.0859 mV) on the 300–500 ms window. N400 effect was observed on 300–500 ms window from the grand average waveforms of each electrode of interest. ANOVA analysis identified a significant main effect: decreased alpha power during creative ideation, especially over (O1/2, P7/8) parietooccipital temporal area. Machine learning is used to classify the specific temporal area data’s neural responses (creative, nonsense, and common). A k-nearest neighbors (kNN) classifier was used, and results were evaluated in terms of accuracy, precision, recall, and F1- score using the collected datasets from the participants. With an overall 99.92% accuracy and area under the curve at 0.9995, the kNN classifier successfully classified the participants’ neural responses. These results have great potential for broader adaptation of machine learning techniques in creativity research.

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Work in Progress: Using Neuro-responses to Understand Creativity, the Engineering Design Process, and Concept Generation

Cited by 3 publications

References 36 publications

Event Related Potentials (ERP) Study to Understand Function to Object Mapping for Engineering Student

Event Related Potentials (ERP) Study to Understand Function to Object Mapping for Engineering Student

Industry 4.0 Applications for Medical/Healthcare Services

Electroencephalogram Experimentation to Understand Creativity of Mechanical Engineering Students

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