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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

Chinese

该研究调查了是否短,最少预处理的EEG细分包含足够的信息,以区分根据迈尔斯 - 布里格斯类型指标(MBTI)分类为外向或内向的个体。50名参与者的EEG记录被划分为4个细分,并使用主体独立的嵌入式5倍交叉验证程序进行分析,以比较EEGNet,EEGProgress和新提议的紧凑合并架构SSCNet。SSCNet获得了最高的平均精度(0.6481),AUC (0.7063),和F1分数(0.6276).拟议的架构也显示了在折叠之间最低的变量。SSCNet展示了最佳的计算效率,最短的训练中期时间和最高的训练效率,尽管有类似可编号的训练

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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