Detecting Unfavorable Driving States in Electroencephalography Based on a PCA Sample Entropy Feature and Multiple Classification Algorithms. - Tao Zhang; Hong Wang; Jichi Chen; Enqiu He (Entropy, 2020)

Recognizing drowsiness in young men during real driving based on electroencephalography using an end-to-end deep learning approach. - Jichi Chen; Shjie Wang; Enqiu He; Hong Wang; Lin Wang (Biomed. Signal Process. Control., 2021)

Two-dimensional phase lag index image representation of electroencephalography for automated recognition of driver fatigue using convolutional neural network. - Jichi Chen; Shijie Wang; Enqiu He; Hong Wang; Lin Wang (Expert Syst. Appl., 2022)

Convolutional neural network with transfer learning approach for detection of unfavorable driving state using phase coherence image. - Jichi Chen; Hong Wang; Shjie Wang; Enqiu He; Tao Zhang; Lin Wang (Expert Syst. Appl., 2022)

A novel approach for cross-subject unfavorable driving state detection by cooperation between phase-based functional connectivity and convolutional neural network. - Jichi Chen; Hong Wang; Enqiu He (Biomed. Signal Process. Control., 2023)

A Transfer Learning-Based CNN Deep Learning Model for Unfavorable Driving State Recognition. - Jichi Chen; Hong Wang; Enqiu He (Cogn. Comput., 2024)

Deep learning approach for detection of unfavorable driving state based on multiple phase synchronization between multi-channel EEG signals. - Jichi Chen; Yuguo Cui; Hong Wang; Enqiu He; Adi Alhudhaif (Inf. Sci., 2024)