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arXiv Machine Learning By Puneet Kumar, Winson F. Z. Yang, Alakhsimar Singh, Xiaobai Li, Matthew D. Sacchet

Machine Learning-Based Classification of Jhana Advanced Concentrative Absorption Meditation Using 7 Tesla Functional Magnetic Resonance Imaging

Read the original on arXiv Machine Learning →

The study uses 7‑Tesla fMRI data from 20 advanced meditators to test whether regional homogeneity (ReHo) patterns can classify Jhana advanced concentrative absorption meditation (ACAM‑J) states with machine learning. Across 19 binary comparisons, an ensemble of six classifiers achieved an average accuracy of about 66 % and a Cohen’s κ of 0.24, with the highest discrimination between the most distinct states (ACAM‑J1 vs ACAM‑J6). Prefrontal and anterior cingulate regions were the most influential features in the models.

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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