@article{ref1,
title="Research on fatigue driving detection technology based on CA-ACGAN",
journal="Brain sciences",
year="2024",
author="Ye, Han and Chen, Ming and Feng, Guofu",
volume="14",
number="5",
pages="e436-e436",
abstract="Driver fatigue represents a significant peril to global traffic safety, necessitating the advancement of potent fatigue monitoring methodologies to bolster road safety. This research introduces a conditional generative adversarial network with a classification head that integrates convolutional and attention mechanisms (CA-ACGAN) designed for the precise identification of fatigue driving states through the analysis of electroencephalography (EEG) signals. First, this study constructed a 4D feature data model capable of mirroring drivers' fatigue state, meticulously analyzing the EEG signals' frequency, spatial, and temporal dimensions. Following this, we present the CA-ACGAN framework, a novel integration of attention schemes, the bottleneck residual block, and the Transformer element. This integration was designed to refine the processing of EEG signals significantly. In utilizing a conditional generative adversarial network equipped with a classification header, the framework aims to distinguish fatigue states effectively. Moreover, it addresses the scarcity of authentic data through the generation of superior-quality synthetic data. Empirical outcomes illustrate that the CA-ACGAN model surpasses various extant methods in the fatigue detection endeavor on the SEED-VIG public dataset. Moreover, juxtaposed with leading-edge GAN models, our model exhibits an efficacy in in producing high-quality data that is clearly superior. This investigation confirms the CA-ACGAN model's utility in fatigue driving identification and suggests fresh perspectives for deep learning applications in time series data generation and processing.<p /> <p>Language: en</p>",
language="en",
issn="2076-3425",
doi="10.3390/brainsci14050436",
url="http://dx.doi.org/10.3390/brainsci14050436"
}