EEG-Based Cognitive Load Comparison in Construction Sensor Data Analytics

9 pages•Published: May 26, 2024

Mohammad Khalid, Abiola Akanmu, Akinwale Okunola, Ibukun Awolusi and Homero Murzi

Abstract

With rising interest in innovative construction methodologies, global construction companies are actively exploring emerging sensing technologies and employing data analytics techniques to draw insights and improve their operations. While numerous educational disciplines employ Block-based Programming Interfaces to enhance domain-specific data-related inquiry and visualization skills, the construction sector has yet to fully explore this practical approach. Introducing block interfaces in construction education may overwhelm newcomers with excessive cognitive load. Past research has primarily relied on subjective measures, overlooking objective indicators for assessing cognitive responses to block interfaces’ interaction elements. This study evaluates the cognitive load induced using InerSens, a Block Programming Interface designed to address authentic construction challenges in ergonomic risk assessment. Electroencephalography is utilized to measure cognitive load, and the results are compared to those of a traditional tool, Excel. Theta Power Spectral Density in the frontal brain region, an indicator of cognitive load, demonstrates that in four out of six tasks, InerSens incurs lower cognitive load than Excel. The findings of this study underscore the potential of InerSens as a viable tool in managing cognitive load efficiency, paving the way for more effective and streamlined sensor data analytics learning experiences for future construction professionals.

Keyphrases: block based programming interface, cognitive load, construction safety, eeg, sensor data analytics

In: Tom Leathem, Wes Collins and Anthony Perrenoud (editors). Proceedings of 60th Annual Associated Schools of Construction International Conference, vol 5, pages 184-192.

Links:	https://easychair.org/publications/paper/jXqz
	https://doi.org/10.29007/48rn

BibTeX entry

@inproceedings{ASC2024:EEG_Based_Cognitive_Load,
  author    = {Mohammad Khalid and Abiola Akanmu and Akinwale Okunola and Ibukun Awolusi and Homero Murzi},
  title     = {EEG-Based Cognitive Load Comparison in Construction Sensor Data Analytics},
  booktitle = {Proceedings of 60th Annual Associated Schools of Construction International Conference},
  editor    = {Tom Leathem and Wes Collins and Anthony Perrenoud},
  series    = {EPiC Series in Built Environment},
  volume    = {5},
  publisher = {EasyChair},
  bibsource = {EasyChair, https://easychair.org},
  issn      = {2632-881X},
  url       = {/publications/paper/jXqz},
  doi       = {10.29007/48rn},
  pages     = {184-192},
  year      = {2024}}

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