L-FAME: Longitudinal Focused Attention Meditation EEG Dataset and Benchmark
Angqi Li, Ab Basit Rafi Syed, Hamzeh Alzweri, Taosheng Liu, Barry H. Cohen, Saiprasad Ravishankar
arXiv (Cornell University) May 21, 2026 preprint DOI: 10.48550/arxiv.2605.22893 (opens in new tab)
Study at a glance
AI-extracted from the abstract| Characteristics | Randomized controlled trial Longitudinal |
|---|---|
| Sample size | 74 |
| Population | Healthy college participants |
| Interventions | SA-TA-NA-MA Hare Krishna Breath Focus |
| Duration | Six-week training period |
| Topics | Meditation |
| Keywords | Benchmark surveying Electroencephalography Preprocessor Suite Generalization Baseline sea Longitudinal study Artificial intelligence Machine learning Cognitive psychology Cognition Deep learning Range aeronautics Artificial neural network |
| Key findings | The L-FAME dataset enables three classification tasks—cognitive state decoding, fine-grained meditation technique classification, and cross-session adaptation—with baseline results from classical and deep learning models. |
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: two mantra-based techniques (SA-TA-NA-MA and Hare Krishna) and one Breath Focus practice. Leveraging this unique longitudinal and comparative dataset, we propose a benchmark suite comprising three distinct classification tasks: (1) cognitive state decoding to distinguish between resting and meditation states, (2) fine-grained classification of the specific meditation techniques, and (3) cross-session adaptation to evaluate model generalization across the longitudinal time gap. We provide comprehensive baseline results for these tasks utilizing a range of classical machine learning algorithms and deep learning architectures. The complete dataset, preprocessing pipelines, and benchmark evaluation code will be publicly released, offering a valuable resource and a standardized framework for the development and comparison of new analytical methods in computational meditation research and EEG-based machine learning. The dataset is available at https://huggingface.co/datasets/L-FAME-Dataset-Benchmark/L-FAME