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arxiv: 2605.22893 · v1 · pith:P56D5ZYZnew · submitted 2026-05-21 · 📡 eess.SP · cs.LG

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 This is my paper

Pith reviewed 2026-05-25 05:45 UTC · model grok-4.3

classification 📡 eess.SP cs.LG
keywords EEG datasetfocused attention meditationlongitudinal studyclassification benchmarkmachine learningmeditation techniquescognitive state decodingpublic dataset
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The pith

A new EEG dataset from 74 participants across two sessions supports benchmarks for decoding meditation states, identifying specific techniques, and adapting models over six weeks of training.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper introduces the L-FAME dataset of EEG recordings and psychological assessments collected from 74 healthy college students at two time points before and after six weeks of training. Participants were assigned to one of three focused attention practices: two mantra-based techniques or breath focus. The authors define a benchmark with three tasks that test models on distinguishing rest from meditation, classifying which technique is used, and generalizing across the pre-to-post time gap. They report baseline results from classical machine learning and deep learning methods on these tasks. The dataset, preprocessing code, and evaluation scripts are scheduled for public release to enable standardized comparisons in EEG analysis of meditation.

Core claim

The authors introduce the L-FAME dataset containing pre-intervention and post-intervention EEG recordings from 74 participants randomly assigned to SA-TA-NA-MA, Hare Krishna, or Breath Focus meditation, together with a benchmark suite of three classification tasks: cognitive state decoding between resting and meditation, fine-grained identification of the specific meditation technique, and cross-session adaptation across the six-week interval, along with baseline performance numbers obtained from a range of classical and deep learning classifiers.

What carries the argument

The L-FAME dataset and its three-task benchmark, which uses the longitudinal pre/post design and the three distinct meditation groups to create labeled EEG examples for state decoding, technique classification, and temporal generalization.

If this is right

  • Researchers can evaluate new models on their ability to decode cognitive states from EEG during meditation versus rest.
  • The fine-grained task allows direct comparison of how well algorithms distinguish between specific mantra and breath practices.
  • The cross-session task measures whether models trained on pre-intervention data generalize to post-intervention recordings after training.
  • Public release of the full dataset and code creates a shared testbed for new preprocessing methods and classifiers in meditation EEG research.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • If the benchmarks prove solvable, the dataset could support development of real-time neurofeedback systems that guide users toward particular meditation states.
  • Longitudinal labels may enable studies of how neural signatures of a given practice change with repeated training.
  • The three-group design could be extended to test whether models trained on one meditation type transfer to another.

Load-bearing premise

The three chosen meditation practices and the two-session design produce distinguishable EEG patterns that the classification tasks can meaningfully separate.

What would settle it

Baseline models achieving only chance-level accuracy on all three tasks, or statistical tests showing no reliable EEG differences between rest and meditation or between the three techniques, would show the dataset does not support the intended benchmarks.

Figures

Figures reproduced from arXiv: 2605.22893 by Ab Basit Rafi Syed, Angqi Li, Barry H. Cohen, Hamzeh Alzweri, Saiprasad Ravishankar, Taosheng Liu.

Figure 1
Figure 1. Figure 1: Overview of Longitudinal Meditation Study Design and EEG Task Sequence Interventions. Participants were trained in specific techniques categorized under FAM. These variants included focusing on sensations created by breathing, referred to here as Breath Focus, and Japa (mantra-based meditation), which utilized the mantras SA-TA-NA-MA and Hare Krishna. EEG Recording setup. EEG data were recorded in a contro… view at source ↗
Figure 2
Figure 2. Figure 2: Distributions of (A) age, (B) sex, and (C) handedness demonstrate consistent demographic [PITH_FULL_IMAGE:figures/full_fig_p015_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Analysis of Group Assignment and Demographics as Potential Causes of Dropout and Attrition, Detailing the Distributions of Sex, Group, Handedness, and Age With Corresponding p-Values for Each Analysis Pre-Intervention EEG Oscillatory Profiles and Connectivity Fail to Predict Participant Attrition To rigorously evaluate whether baseline neurophysiological states bias experimental retention, we analyzed 30 p… view at source ↗
Figure 4
Figure 4. Figure 4: Receiver Operating Characteristic (ROC) curve for dropout prediction using pre-intervention EEG features. The combination of non-significant univariate band-level differences and near-chance multi￾variate classification performance provides di￾rect evidence against a Missing Not At Random (MNAR) mechanism. These results indicate that baseline neural representations carry effectively no predictive informati… view at source ↗
Figure 5
Figure 5. Figure 5: Pre-intervention absolute EEG band power distributions for completers vs. dropouts. Compari￾son of log10-transformed absolute power spectral density (PSD) between participants who completed the study (n = 44, teal) and those who dropped out (n = 30, orange). Results are shown for five canonical frequency bands under both Resting State (top row) and Active Meditation (bottom row) conditions. Significance br… view at source ↗
Figure 6
Figure 6. Figure 6: Diagram of the Longitudinal Meditation Benchmark pipeline. Link widths are proportional [PITH_FULL_IMAGE:figures/full_fig_p018_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: Total PSS scores at baseline and follow-up for SA, HK, and BF. [PITH_FULL_IMAGE:figures/full_fig_p021_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: Mean MAIA-2 scores at baseline and follow-up for SA, HK, and BF on eight domains. [PITH_FULL_IMAGE:figures/full_fig_p022_8.png] view at source ↗
Figure 9
Figure 9. Figure 9: Mean FFMQ-SF scores at baseline and follow-up for SA, HK, and BF on five facets. [PITH_FULL_IMAGE:figures/full_fig_p022_9.png] view at source ↗
Figure 10
Figure 10. Figure 10: Chronological split and block-wise inter￾leaved split(Ours) for intra-subject data splitting. Temporal Partitioning And Class Balancing At 250 Hz, the recordings yield 64 × 1000 fea￾ture matrices across 64 channels. For intra-subject evaluations, the data are partitioned into 20-second blocks, alternating between training and testing sets in an 80%/20% ratio. Unlike standard chrono￾logical splitting, this… view at source ↗
Figure 11
Figure 11. Figure 11: Intra-Subject Evaluation Protocol Comparison: Block-Split vs. Chronological-Split (N = 74) (a) Mean AUC (%) and standard deviation across all subjects for four models under two intra-subject protocols: Intra-Block (random fold assignment) and Intra-Chrono (temporally ordered folds). ∆ denotes the performance gap between protocols. (b1–b4) Per-subject scatter plots for each model; each point represents one… view at source ↗
Figure 12
Figure 12. Figure 12: Intra-Block AUC Across All Subjects and Models (N = 74) Each spoke represents one subject, arranged clockwise in ascending order of mean Intra-Block AUC averaged across all four models; subjects in the lower-performing quartile (Q1) appear first in the sweep. The four coloured lines correspond to the four model architectures: SCN (ShallowConvNet), DCN (DeepConvNet), EEGNet, and Conformer (EEG-Conformer). … view at source ↗
Figure 13
Figure 13. Figure 13: UMAP visualization of EEGNet penultimate-layer representations for the 44 paired subjects before (left) and after (right) the six-week meditation intervention, with separate models trained for before and after intervention. Each point represents one 4-second EEG window, colored by meditation technique (HK: red, SA: blue, BF: green). UMAP is fitted jointly on the combined features so that both panels share… view at source ↗
Figure 14
Figure 14. Figure 14: Pre-intervention and post-intervention representational similarity analysis (RSA) matrices for EEGNet illustrate the cosine similarity between the neural representation centroids of three techniques (HK, SA, and BF), where both numerical values and color gradients denote the corresponding similarity scores. resulting performance distributions across all evaluated architectures reveal a stark asymmetry in … view at source ↗
Figure 15
Figure 15. Figure 15: Task 2 One-vs-All Classification Performance by Target Group (a) AUC (%) and (b) balanced accuracy (BAcc, %) for each target group (BF, HK, SA) under the inter-subject One-Versus-All (OvA) protocol. Each box shows the median (thick black line), interquartile range (IQR), and 1.5×IQR whiskers; individual runs are overlaid as jittered dots coloured by model architecture (SCN: ShallowConvNet; DCN: DeepConvNe… view at source ↗
Figure 17
Figure 17. Figure 17: Per-Subject Comparison of Full Fine-Tuning vs. Linear Probing at 30 Shots (N = 44 subjects). Each point represents one subject–model pair (44 × 4 = 176 observations); the x-axis shows AUC (%) under linear probing and the y-axis under full fine-tuning. Points are coloured by model architecture (SCN: ShallowConvNet; DCN: DeepConvNet). The dashed diagonal line marks equality (y = x): points above in￾dicate t… view at source ↗
read the original 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

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 2 minor

Summary. The paper introduces the L-FAME dataset consisting of EEG recordings and psychological assessments from 74 healthy college participants at two time points (pre- and post-intervention) across three meditation groups (SA-TA-NA-MA, Hare Krishna, and Breath Focus). It defines a benchmark suite with three tasks—cognitive state decoding (rest vs. meditation), fine-grained technique classification, and cross-session adaptation—and supplies baseline results using classical ML algorithms and deep learning architectures. The dataset, preprocessing pipelines, and evaluation code are to be publicly released.

Significance. If the dataset and baselines hold as described, this provides a valuable standardized resource for longitudinal and comparative studies of focused attention meditation in EEG, addressing a gap in public multi-technique longitudinal data. The explicit public release of data, pipelines, and code is a clear strength that supports reproducibility and community use in computational meditation research and EEG ML.

major comments (2)
  1. [Benchmark suite definition] The section defining the benchmark suite does not explicitly detail how the three meditation states were segmented from continuous recordings or how labels were assigned for the state-decoding and technique-classification tasks; this information is load-bearing for interpreting the baseline results and ensuring the tasks evaluate the claimed distinctions.
  2. [Methods / Dataset description] Participant allocation is described as random assignment to groups, but no per-group counts, demographic stratification, or power analysis is provided; this affects assessment of whether the fine-grained technique classification task has sufficient balance to support the reported baselines.
minor comments (2)
  1. [Abstract] The abstract states that comprehensive baseline results are supplied but includes no numerical accuracies, error bars, or key findings; adding a sentence with headline metrics would improve the standalone readability of the abstract.
  2. [Figures] Figure captions for any EEG topoplots or performance plots should explicitly state the number of participants and sessions contributing to each panel.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive review and positive recommendation of minor revision. We address each major comment below and will revise the manuscript accordingly to improve clarity and completeness.

read point-by-point responses

  1. Referee: [Benchmark suite definition] The section defining the benchmark suite does not explicitly detail how the three meditation states were segmented from continuous recordings or how labels were assigned for the state-decoding and technique-classification tasks; this information is load-bearing for interpreting the baseline results and ensuring the tasks evaluate the claimed distinctions.

    Authors: We agree that explicit segmentation and labeling details are necessary for full interpretability. The current manuscript describes the three tasks at a high level in the benchmark suite section. In the revision we will add a dedicated subsection that specifies the use of event markers to segment meditation periods from continuous recordings, the exact temporal windows extracted for each state, and the label assignment rules for both the cognitive-state (rest vs. meditation) and technique-classification tasks. revision: yes

  2. Referee: [Methods / Dataset description] Participant allocation is described as random assignment to groups, but no per-group counts, demographic stratification, or power analysis is provided; this affects assessment of whether the fine-grained technique classification task has sufficient balance to support the reported baselines.

    Authors: We acknowledge the value of these details for evaluating task balance. The manuscript states that participants were randomly assigned; we will expand the participant section to report the exact per-group counts, summarize available demographic variables, and include a brief note on sample-size considerations drawn from prior EEG meditation literature. revision: yes

Circularity Check

0 steps flagged

No significant circularity

full rationale

The paper is a dataset release and benchmark paper whose central claim is the public introduction of the L-FAME EEG recordings, psychological assessments, preprocessing pipelines, and three defined classification tasks (cognitive state decoding, meditation technique classification, cross-session adaptation) together with baseline ML/DL results. No mathematical derivations, equations, fitted parameters, or predictions appear in the provided text. The baseline results are empirical evaluations on the released data rather than quantities that reduce to the inputs by construction. None of the six enumerated circularity patterns apply; the work is self-contained as a resource contribution whose validity does not depend on internal self-definition or self-citation chains.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

This is a dataset introduction paper; no mathematical derivations, fitted parameters, or new postulated entities are required. The central contribution is the collection and release of recordings rather than any model or equation.

pith-pipeline@v0.9.0 · 5785 in / 1145 out tokens · 16965 ms · 2026-05-25T05:45:07.569077+00:00 · methodology

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