Affordable EEG, Actionable Insights: An Open Dataset and Evaluation Framework for Epilepsy Patient Stratification

TL;DR

This paper introduces NEUROSKY-EPI, an open dataset of consumer-grade EEG for epilepsy, and proposes EmbedCluster, a pipeline for patient stratification that leverages low-cost EEG data to support accessible epilepsy care.

Contribution

It provides the first open single-channel EEG dataset for epilepsy in a South Asian setting and develops a novel unsupervised clustering pipeline for patient stratification using this data.

Findings

Low-cost, single-channel EEG can support meaningful patient stratification.

The EmbedCluster pipeline effectively clusters patients based on EEG patterns.

The dataset and methods promote accessible epilepsy diagnostics in resource-limited settings.

Abstract

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.