Drowsy Driver Detection by EEG Analysis Using Fast Fourier Transform

TL;DR

This paper presents an automatic EEG-based method for detecting driver drowsiness using Fast Fourier Transform to analyze EEG signals, aiming to improve road safety by early detection of drowsiness.

Contribution

The study introduces a novel EEG analysis approach using FFT for drowsiness detection, tested on sleep-EDF database samples, enhancing existing methods.

Findings

FFT-based EEG analysis effectively identifies drowsiness levels.

The method shows promising results on Physionet sleep-EDF data.

Potential for real-time driver drowsiness monitoring.

Abstract

In this paper, we try to analyze drowsiness which is a major factor in many traffic accidents due to the clear decline in the attention and recognition of danger drivers. The object of this work is to develop an automatic method to evaluate the drowsiness stage by analysis of EEG signals records. The absolute band power of the EEG signal was computed by taking the Fast Fourier Transform (FFT) of the time series signal. Finally, the algorithm developed in this work has been improved on eight samples from the Physionet sleep-EDF database.