Early Detection of Human Decision-Making in Concealed Object Visual Searching Tasks: An EEG-BiLSTM Study
- Publisher:
- IEEE
- Publication Type:
- Conference Proceeding
- Citation:
- 2023 45th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), 2023, 2023, pp. 1-4
- Issue Date:
- 2023-12-11
Open Access
Copyright Clearance Process
- Recently Added
- In Progress
- Open Access
This item is open access.
Detecting concealed objects presents a significant challenge for human and artificial intelligent systems Detecting concealed objects task necessitates a high level of human attention and cognitive effort to complete the task successfully Thus in this study we use concealed objects as stimuli for our decision making experimental paradigms to quantify participants decision making performance We applied a deep learning model Bi directional Long Short Term Memory BiLSTM to predict the participant s decision accuracy by using their electroencephalogram EEG signals as input The classifier model demonstrated high accuracy reaching 96 1 with an epoching time range of 500 ms following the stimulus event onset The results revealed that the parietal occipital brain region provides highly informative information for the classifier in the concealed visual searching tasks Furthermore the neural mechanism underlying the concealed visual searching and decision making process was explained by analyzing serial EEG components The findings of this study could contribute to the development of a fault alert system which has the potential to improve human decision making performance
Please use this identifier to cite or link to this item: