Electroencephalogram recordings in Jhana states: An open dataset
Marco S. Fabus, Stephen Zerfas, Alex Gruver, Maria Fini, Tamaz Gadaev, Kathryn J. Devaney
bioRxiv (Cold Spring Harbor Laboratory) July 2, 2026 DOI: 10.64898/2026.06.27.734949 via OpenAlex
Summary
AI-generated from the abstractJhana meditation produces states of self-reinforcing bliss that may be useful in clinical and scientific settings, but research has been limited by scarce data and few expert practitioners. To address this, the authors release the largest open-access dataset of electroencephalographic and physiological recordings from expert Jhana meditators, comprising over 100 hours of data from 26 subjects across three retreats. The dataset includes example analysis code. This resource aims to enable broader collaboration and advance understanding of internally generated altered states of consciousness.
Study at a glance
| Characteristics | Observational cohort Peer reviewed |
|---|---|
| Sample size | 26 |
| Population | Expert Jhana meditators |
| Topics | Meditation |
| Keywords | Variety cybernetics Code set theory Electroencephalography Computer science |
| Key finding | The release of the largest open-access dataset of EEG and physiological recordings from expert Jhana meditators can facilitate research into endogenously generated altered states of consciousness. |
Abstract
Abstract The use of meditation as a tool to improve human wellbeing is receiving considerable scientific interest. However, most existing research has focused on concentration-based practices. One powerful alternative is jhana meditation, which leads to states characterised by self-reinforcing bliss, potentially useful for a variety of clinical and scientific domains. However, our understanding of these states is limited by small amounts of data and poor access to experts. To enable new insights, here we release the largest to date and first open-access dataset of electroencephalographic and physiological recordings in expert Jhana meditators. This includes 100+ hours of data in N=26 subjects across three retreats, alongside a detailed description and example code illustrating analysis of the data. This open dataset release can enable wider collaboration and has the potential to move us closer to an understanding of endogenously generated altered states of consciousness.