构建6周冥想研究数据集，分析脑电变化与不同冥想效果。
L-FAME: Longitudinal Focused Attention Meditation EEG Dataset and Benchmark
- 采集74名大学生前后两次脑电与心理数据，对比三种冥想方式。
- 提出三类任务：状态识别、冥想类型分类、跨时段模型泛化测试。
- 公开数据与代码，助力脑电与正念研究的标准化评估。
我们介绍了一个新颖的纵向专注冥想脑电（L-FAME）数据集及配套基准，旨在推动对不同冥想实践神经效应及其在六周训练期内演变的研究。该数据集包含74名健康大学生在干预前与干预后两个时间点的脑电记录和心理评估，参与者被随机分配至三个冥想组：两种曼陀罗冥想（SA-TA-NA-MA 和 Hare Krishna）以及一种呼吸专注练习。基于这一独特的时间序列对比数据集，我们提出了一个包含三项分类任务的基准套件：(1) 认知状态解码，区分静息与冥想状态；(2) 冥想技术的细粒度分类；(3) 跨会话适应，评估模型在纵向时间间隔下的泛化能力。我们提供了多种经典机器学习算法与深度学习架构的完整基线结果。数据集、预处理流程与基准评估代码将全部公开，为计算冥想研究和基于脑电的机器学习方法的开发与比较提供宝贵资源与标准化框架。数据集地址：https://huggingface.co/datasets/L-FAME-Dataset-Benchmark/L-FAME
原文摘要 · Abstract (English)
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
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