Sleepy Driver EEG Brainwave Dataset

1) Data Introduction

• The Sleepy Driver EEG Brainwave Data Dataset is a structured time-series dataset consisting of EEG signals collected using the NeuroSky MindWave sensor, capturing the brainwave activity of drivers in two states: awake and sleepy.

2) Data Utilization

(1) Characteristics of the Sleepy Driver EEG Brainwave Data Dataset: • The dataset is structured for binary classification based on the label indicating whether the driver is sleepy or not, making it well-suited for cognitive state detection tasks such as drowsiness detection. (2) Applications of the Sleepy Driver EEG Brainwave Data Dataset: • Drowsiness detection model training: The dataset can be used to train AI models that classify a driver’s drowsy state in real time based on EEG brainwave patterns. • Brain-Computer Interface (BCI) research: EEG-based drowsiness recognition techniques can be applied to the design of BCI-driven alert systems and driver-assist systems, contributing to the development of smart cognitive systems.