Drowsy Driver Detection by EEG Analysis Using Fast Fourier Transform
This addresses drowsiness detection for drivers to potentially reduce traffic accidents, but it is incremental as it applies an existing method to a specific dataset.
The paper tackled the problem of drowsy driver detection by analyzing EEG signals using Fast Fourier Transform to compute absolute band power, resulting in an algorithm tested on eight samples from the Physionet sleep-EDF database.
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.