Machine learning‐based classifying of risk‐takers and risk‐aversive individuals using resting‐state EEG data: A pilot feasibility study

BACKGROUND: Decision‐making is vital in interpersonal interactions and a country's economic and political conditions. People, especially managers, have to make decisions in different risky situations. There has been a growing interest in identifying managers’ personality traits (i.e., risk‐taking or risk‐averse) in recent years. Although there are findings of signal decision‐making and brain activity, the implementation of an intelligent brain‐based technique to predict risk‐averse and risk‐taking managers is still in doubt. METHODS: This study proposes an electroencephalogram (EEG)‐based intelligent system to distinguish risk‐taking managers from risk‐averse ones by recording the EEG signals from 30 managers. In particular, wavelet transform, a time‐frequency domain analysis method, was used on resting‐state EEG data to extract statistical features. Then, a two‐step statistical wrapper algorithm was used to select the appropriate features. The support vector machine classifier, a supervised learning method, was used to classify two groups of managers using chosen features. RESULTS: Intersubject predictive performance could classify two groups of managers with 74.42% accuracy, 76.16% sensitivity, 72.32% specificity, and 75% F1‐measure, indicating that machine learning (ML) models can distinguish between risk‐taking and risk‐averse managers using the features extracted from the alpha frequency band in 10 s analysis window size. CONCLUSIONS: The findings of this study demonstrate the potential of using intelligent (ML‐based) systems in distinguish between risk‐taking and risk‐averse managers using biological signals.