arXiv:2605.22893v1 Announce Type: cross Abstract: We introduce a novel Longitudinal Focused Attention Meditation Electroencephalography (L-FAME) dataset and an accompanying benchmark, designed to foster research into the neural effects of various meditation practices and the evolution of these effects over a six-week training period. The dataset contains EEG recordings and psychological assessments from 74 healthy college participants, collected at two distinct time points: pre-intervention and post-intervention. Participants were randomly assigned to one of three distinct meditation groups: t
The proliferation of AI and advanced sensing technologies, including EEG, is creating new opportunities to systematically study complex human states like meditation.
This dataset provides a standardized resource for investigating the neurological effects of meditation, potentially leading to more effective mental health interventions and enhanced human-computer interfaces.
The availability of a longitudinal and benchmarked EEG dataset for focused attention meditation allows for more rigorous and comparable research in the neuroscientific and AI communities.
- · Neuroscience researchers
- · AI developers in BCI/health
- · Mental wellness technology companies
- · Ad-hoc meditation study methodologies
Systematic understanding of meditation's neural correlates advances.
Development of personalized and adaptive meditation training programs accelerates, potentially incorporating biofeedback via AI.
Integration of neurobiological insights from meditation into broader AI models for cognitive enhancement or mental state regulation becomes more feasible.
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