A hybrid critical channels and optimal feature subset selection framework for EEG fatigue recognition.

Fatigue driving is one of the potential factors threatening road safety, and monitoring drivers' mental state through electroencephalography (EEG) can effectively prevent such risks. In this paper, a new model, DE-GFRJMCMC, is proposed for selecting critical channels and optimal feature subsets from EEG data to improve the accuracy of fatigue driving recognition. The model is validated on the SEED-VIG dataset.

Key factors capturing the willingness to use automated vehicles for travel in China.

With the continuous advancement of technology, automated vehicle technology is progressively maturing. It is crucial to comprehend the factors influencing individuals' intention to utilize automated vehicles. This study examined user willingness to adopt automated vehicles.