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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

Indonesian

Studi ini memeriksa apakah segmen EEG pendek, minimal pra-proses mengandung informasi yang cukup untuk membedakan antara individu yang diklasifikasikan sebagai ekstrovert atau introvert menurut Indikator Tipe Myers-Briggs (MBTI). Rekaman EEG dari 50 peserta dibagi menjadi 4 segmen dan dianalisis menggunakan prosedur validasi lurus 5 kali yang terpasang subjek independen untuk membandingkan EEGNet, EEGProgress, dan arsitektur konvolusi kompak yang baru diusulkan, SSCNet. SSCNet mencapai akurasi rata-rata tertinggi (0.6481), AUC (0.7063), dan skor F1 (0.6276). Arsitektur yang diusulkan juga menunjukkan variabilitas terendah di antara lipatan. SSCNet menunjukkan efisiensi komputasi terbaik, dengan waktu median pelatihan terpendek dan pelatihan yang

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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