BEED: Bangalore EEG Epilepsy Dataset
About
The Bangalore EEG Epilepsy Dataset (BEED) is a comprehensive EEG collection for epileptic seizure detection and classification. Recorded at a neurological research centre in Bangalore, India, it features high-fidelity EEG signals captured using the standard 10-20 electrode system at a 256 Hz sampling rate. BEED contains 16,000 segments of 20-second EEG recordings evenly distributed across four categories: Healthy Subjects (0), Generalized Seizures (1), Focal Seizures (2), and Seizure Events (3), where seizure activity occurs with events like eye blinking, nail biting, or staring. Each category includes data from 20 adult subjects (ages 21-55) with equal gender representation. The dataset comprises 16 EEG channels (X1-X16) corresponding to different brain regions, with a binary label (y) indicating seizure presence (1) or absence (0). BEED supports machine learning in seizure detection, epilepsy analysis, and EEG research with its balanced, high-resolution data.
Subject Area
Health and Medicine
Instances
8,000
Features
17
Data Types
Tabular, Multivariate
Tasks
Classification
Feature Types
Integer
Features
| Name | Role | Type | Units | Missing Values |
|---|---|---|---|---|
| X1 | Feature | Integer | - | No |
| X2 | Feature | Integer | - | No |
| X3 | Feature | Integer | - | No |
| X4 | Feature | Integer | - | No |
| X5 | Feature | Integer | - | No |
| X6 | Feature | Integer | - | No |
| X7 | Feature | Integer | - | No |
| X8 | Feature | Integer | - | No |
| X9 | Feature | Integer | - | No |
| X10 | Feature | Integer | - | No |
| X11 | Feature | Integer | - | No |
| X12 | Feature | Integer | - | No |
| X13 | Feature | Integer | - | No |
| X14 | Feature | Integer | - | No |
| X15 | Feature | Integer | - | No |
| X16 | Feature | Integer | - | No |
| y | Feature | Binary | - | No |
Introductory Paper
Feature Engineering for Epileptic Seizure Classification Using SeqBoostNet
Najmusseher; P.K. Nizar Banu. 2024.
International Journal of Computing and Digital Systems