EEG Based Decoding of the Perception and Regulation of Taboo Words

TL;DR

This study demonstrates that EEG responses, analyzed with machine learning, can reliably distinguish between neutral, negative, and taboo words, revealing neural signatures associated with emotional word processing.

Contribution

It introduces a novel application of SVM to decode taboo and emotional words from EEG, expanding understanding of cortical responses to taboo language.

Findings

SVM successfully classified word categories from EEG data.

Distinct neural activity patterns associated with late positive potential.

Word categories detectable even during regulation conditions.

Abstract

In daily interactions, emotions are frequently conveyed and triggered through verbal exchanges. Sometimes, we must modulate our emotional reactions to align with societal norms. Among the emotional words, taboo words represent a specific category that has been poorly studied. One intriguing question is whether these word categories can be predicted from EEG responses with the use of machine learning methods. To address this question, Support Vector Machine (SVM) was applied to decode the word categories from Event Related Potential (ERP) in 40 native Italian speakers. 240 neutral, negative and taboo words were used to this aim. Results indicate that the SVM classifier successfully distinguished between the three-word categories, with significant differences in neural activity ascribed to the late positive potential mainly detected in the central-parietal-occipital and anterior right scalp areas in the time windows of 450-649 ms and 650-850 ms. These findings were in line with the established distribution pattern of the late positive potential. Intriguingly, the study also revealed that word categories were still detectable in the regulate condition. This study extends previous results on the domain of the cortical responses of taboo words, and how machine learning methods can be used to predict word categories from EEG responses.