A method for classification NEO–FFI answers fabricated and advantageous due to psychological bias using brainwave specific brain activity networks

The Neuroticism Extraversion Openness–Five Factor Inventory(NEO–FFI) is a psychological scale that assesses personality through five factors. Although widely used, it is susceptible to biases, which reduces reliability. This study proposes a method to classify biased answers using brainwave measured while answering NEO–FFI questions. Brainwave networks are constructed from alpha, beta, delta, theta, and gamma waves measured at eight brain regions (frontal, temporal, parietal, and occipital lobes). Fast Fourier transform and coherence are applied to obtain feature values for nodes and edges of the network. Support vector machines are then trained to classify answers as biased or unbiased based on these features for each personality factor. An experiment with 23 participants aged 19–23 reveals an accuracy of 0.67, precision of 0.72, negative predictive value of 0.62, recall of 0.71, specificity of 0.64, and F1 score of 0.70. This indicates that the proposed method effectively classifies biased answers and improves the reliability of NEO–FFI personality assessments.