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

drowsiness detection; EEG signals; PynqZ2; Zynq SoC; Control and Systems Engineering; Signal Processing; Hardware and Architecture; Computer Networks and Communications; Electrical and Electronic Engineering

Abstract :

[en] 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.

Disciplines :

Electrical & electronics engineering

Author, co-author :

Zayed, Aymen ; Université de Mons - UMONS > Faculté Polytechnique > Service d'Electronique et Microélectronique ; Research Laboratory of Technology and Medical Imaging—LTIM—LR12ES06, Faculty of Medicine of Monastir, Monstir, Tunisia ; National Engineering School of Sousse, University of Sousse, Sousse, Tunisia

Trabes, Emanuel ; Université de Mons - UMONS > Faculté Polytechnique > Service d'Electronique et Microélectronique ; Department of Electronics, Universidad Nacional de San Luis, San Luis, Argentina

Tarrillo, Jimmy ; Service d’électronique et de Microélectronique, University of Mons, Mons, Belgium ; Electrical and Mechatronic Department, Universidad de Ingenieria y Tecnologia, Barranco, Peru

Ben Khalifa, Khaled ; Research Laboratory of Technology and Medical Imaging—LTIM—LR12ES06, Faculty of Medicine of Monastir, Monstir, Tunisia ; Higher Institute of Applied Science and Technology of Sousse, University of Sousse, Sousse, Tunisia

Valderrama, Carlos ; Service d’électronique et de Microélectronique, University of Mons, Mons, Belgium

Language :

English

Title :

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

Publication date :

February 2025

Journal title :

Electronics

ISSN :

2079-9292

eISSN :

2079-9292

Publisher :

Multidisciplinary Digital Publishing Institute (MDPI)

Volume :

Issue :

Pages :

404

Peer reviewed :

Peer reviewed

Additional URL :

https://www.mdpi.com/2079-9292/14/3/404/pdf

Research unit :

Electronics and Microelectronics

Research institute :

R450 - Institut NUMEDIART pour les Technologies des Arts Numériques

Available on ORBi UMONS :

since 09 March 2026

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