NEUROSKY–EPI: The First Open Single–Electrode Epilepsy EEG Dataset with Context–Aware Modeling and Clinically Grounded Metadata

HM Shadman Tabib, Md. Hasnaen Adil, Ayesha Rahman, Ahmmad Nur Swapnil, Maoyejatun Hasana, A. B. M. Alim Al Islam

Keywords: Single-electrode EEG, Epilepsy monitoring, Context-aware modeling, EEGNet, Low-cost consumer device, Open and Novel dataset

TL;DR: We release NEUROSKY–EPI, the first open single–electrode epilepsy EEG dataset with clinical metadata, showing that context–aware modeling improves seizure analytics on low–cost consumer devices.

Abstract: We introduce NEUROSKY–EPI, to our knowledge the first open single-electrode EEG dataset for epilepsy. The resource contains 25 patients and 2,032 labeled 1 s windows captured with an off-the-shelf NeuroSky MindWave Mobile 2 headset, accompanied by clinically interpretable metadata (seizure type, antiepileptic drugs, comorbidities, adherence, education, socioeconomic status). We propose a simple, reproducible evaluation for handy devices: train EEGNet using CHB–MIT windowed into 12,009 8 s segments from 24 patients on a single Fp1 channel to mirror the consumer lead, then evaluate directly on NEUROSKY–EPI band–power features. This practical setup yields ~60% test accuracy; augmenting inputs with a 16–dim context vector from an autoencoder that summarizes resting↔awake responsiveness improves performance to ~68%. Patient–level embeddings from EEGNet’s penultimate layer also show stronger unsupervised separability with context (KMeans: 54.17% → 66.67%). We will release de-identified data, code, and concise dataset/model cards to enable clinically grounded, low-compute epilepsy analytics and fairness-aware studies on single–electrode recordings.

Submission Number: 128