Paving the Way for Future EEG Studies in Construction: Dependent Component Analysis for Automatic Ocular Artifact Removal from Brainwave Signals

Yizhi Liu, Mahmoud Habibnezhad, Shayan Shayesteh, Houtan Jebelli, Sanghyun Lee

Research output: Contribution to journalArticlepeer-review

Abstract

Construction workers' poor mental states can lead to numerous safety and productivity issues. One major trend in construction research is quantitatively evaluating workers' psychophysiological states. With the advances in wearable electroencephalogram (EEG) devices, such assessment can be possible by interpreting workers' brainwave patterns. However, the recorded EEG signals are highly contaminated with signal noises, particularly ocular-related artifacts generated from blinking and eye movement. Although most of the noise can be suppressed by well-established filtering techniques, ocular artifacts cannot be eliminated easily and automatically by conventional techniques. To overcome this challenge, this study proposes a procedure to reduce ocular artifacts by integrating dependence component analysis, image processing, and machine learning algorithms. The results demonstrated the potential of the proposed procedure to produce high-quality EEG signals accurately, continuously, and automatically during construction operations. The findings contribute to the body of knowledge by overcoming the barriers to reliable translation of EEG signals in numerous construction-related investigations, especially those that add substantially to the understanding of the effect of workplace stressors on workers' health and safety.

Original languageEnglish (US)
JournalJournal of Construction Engineering and Management
Volume147
Issue number8
DOIs
StatePublished - Aug 1 2021

All Science Journal Classification (ASJC) codes

  • Civil and Structural Engineering
  • Building and Construction
  • Industrial relations
  • Strategy and Management

Fingerprint

Dive into the research topics of 'Paving the Way for Future EEG Studies in Construction: Dependent Component Analysis for Automatic Ocular Artifact Removal from Brainwave Signals'. Together they form a unique fingerprint.

Cite this