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

Key points

• 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.1
• The proposed architecture also showed the lowest variability across folds.1
• 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.1
• 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.1

Sources (1)

• [1]CNN-based classifier for subject-independent MBTI Extraversion/Introversion preference classification from short-time EEG signals
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 20, 04:02 PM

  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.

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