Early Detection of Human Decision-Making in Concealed Object Visual Searching Tasks: An EEG-BiLSTM Study.

Abstract

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.