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CNN-based classifier for subject-independent MBTI Extraversion/Introversion preference classification from short-time EEG signals

Filip Postepski, Grzegorz M. Wojcik · 2026 · arXiv (Cornell University)

DOI: 10.48550/arxiv.2610.28442 · cited by 0 works (OpenAlex) · open-access PDF

French

Cette étude examine si les segments EEG courts et minimalement pré-traités contiennent suffisamment d'informations pour distinguer les individus classés comme extrovertis ou introvertis selon l'indicateur de type de Myers-Briggs (MBTI). Les enregistrements EEG de 50 participants ont été divisés en 4 segments et analysés à l'aide d'une procédure de validation croisée 5 fois née indépendamment du sujet pour comparer EEGNet, EEGProgress et une architecture convolutive compacte nouvellement proposée, SSCNet. SSCNet a atteint la plus haute précision moyenne (0.6481), AUC (0.7063), et F1-score (0.6276). L'architecture proposée a également montré la variabilité la plus faible entre les

Machine translation (Meta M2M100); the English original follows.

Abstract

This study examines whether short, minimally preprocessed EEG segments contain sufficient information to distinguish between individuals classified as extraverted or introverted according to the Myers--Briggs Type Indicator (MBTI). EEG recordings from 50 participants were divided into 4-s segments and analyzed using a subject-independent nested 5-fold cross-validation procedure to compare EEGNet, EEGProgress, and a newly proposed compact convolutional architecture, SSCNet. SSCNet achieved the highest mean accuracy (0.6481), AUC (0.7063), and F1-score (0.6276). The proposed architecture also showed the lowest variability across folds. SSCNet demonstrated the best computational efficiency, with the shortest median training epoch time and the highest training throughput despite having a similar number of trainable parameters to EEGProgress. The results suggest that MBTI Extraversion--Introversion preferences may be moderately predictable from short time-domain EEG segments without manual spectral feature extraction, while highlighting the importance of subject-independent validation and computationally efficient model design.

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Topics: Electroencephalography, Artificial intelligence, Computer science, Pattern recognition (psychology), Classifier (UML), Speech recognition

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