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arXiv Machine Learning By Filip Postepski, Grzegorz M. Wojcik

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

Read the original on arXiv Machine Learning →

The paper investigates whether brief, minimally processed EEG segments can differentiate individuals labeled as extraverted or introverted by the Myers–Briggs Type Indicator. Using a subject‑independent nested 5‑fold cross‑validation, the authors compared EEGNet, EEGProgress, and a new compact convolutional network called SSCNet. SSCNet achieved the highest mean accuracy (0.6481), AUC (0.7063), and F1‑score (0.6276), exhibited the lowest variability across folds, and proved computationally efficient with the shortest training epoch time and highest throughput.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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