Efficient Embedded System for Drowsiness Detection Based on EEG Signals: Features Extraction and Hardware Acceleration

Abstract

Drowsiness detection is crucial for ensuring the safety of individuals engaged in high-risk activities. Numerous studies have explored drowsiness detection techniques based on EEG signals, but these have typically been validated on computers, which limits their portability. In this paper, we introduce the design and implementation of a drowsiness detection technique utilizing EEG signals, executed on a Zynq7020 System on Chip (SoC) as part of a Pynq-Z2 module. This approach is more suitable for portable applications. We have implemented the Discrete Wavelet Transform (DWT) and feature extraction functions as intellectual property (IP) cores, while other functions run on the ARM processor of the Zynq7020.