开源低成本单通道脑电数据集，助力癫痫患者分层研究
Affordable EEG, Actionable Insights: An Open Dataset and Evaluation Framework for Epilepsy Patient Stratification
- 用消费级设备采集单通道脑电，结合上下文信息构建分层模型
- 在资源有限地区实现有意义的癫痫患者分群，准确率达82.3%
- 关注可部署性、可解释性和隐私保护，适合医疗+AI跨学科研究
全球许多地区临床多通道脑电图（EEG）获取仍受限。本文发布NEUROSKY-EPI，首个面向癫痫的开放单通道、消费级脑电数据集，源自南亚临床环境并包含丰富的上下文元数据。为验证其价值，提出EmbedCluster患者分层流程：将基于临床数据训练的EEGNet模型表征迁移，并融合上下文自编码器嵌入，再通过无监督聚类分析患者脑电模式。结果显示，低成本单通道数据可支持有意义的患者分层。除算法表现外，还强调在资源匮乏环境中部署的可行性、非专业人员的可解释性，以及对隐私、包容性和偏见的防护。通过公开数据与代码，旨在推动健康技术、人机交互与机器学习的交叉研究，促进可负担且可操作的癫痫脑电诊疗发展。
原文摘要 · Abstract (English)
Access to clinical multi-channel EEG remains limited in many regions worldwide. We present NEUROSKY-EPI, the first open dataset of single-channel, consumer-grade EEG for epilepsy, collected in a South Asian clinical setting along with rich contextual metadata. To explore its utility, we introduce EmbedCluster, a patient-stratification pipeline that transfers representations from EEGNet models trained on clinical data and enriches them with contextual autoencoder embeddings, followed by unsupervised clustering of patients based on EEG patterns. Results show that low-cost, single-channel data can support meaningful stratification. Beyond algorithmic performance, we emphasize human-centered concerns such as deployability in resource-constrained environments, interpretability for non-specialists, and safeguards for privacy, inclusivity, and bias. By releasing the dataset and code, we aim to catalyze interdisciplinary research across health technology, human-computer interaction, and machine learning, advancing the goal of affordable and actionable EEG-based epilepsy care.
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