Ce Zhang

Room 406

635 Prices Fork Road

Blacksburg, VA, 24060

Email: zce@vt.edu

I am a Ph.D student in Virginia Tech with highly enthusiastic in autonomous vehicle, machine learning/deep learning algorithms, and robotics. I earned my Bachelor’s Degree from Virginia Tech Mechanical Engineering Department at 2018 and continue my research on autonomous vehicle in Virginia Tech ASIM Lab with Dr. Azim Eskandarian.

In the past, I have conducted my research on semi-truck lateral dynamic behavior and human cognitive state analysis for autonomous driving.

Currently, my research focus is autonomous vehicle’s sensor perception quality assessment and evaluation.

news

Mar 11, 2022	I will join Motional as the deep learning intern in this summer!
Sep 22, 2021	I am thankfully to receive the best student paper award from IEEE ITSC 2021.
Mar 25, 2021	Our paper about EEG motor imagery is published by IOP-Science Journal of Neural Engineering.
Sep 2, 2020	Our survey paper about EEG for driver state analysis is published by IEEE/CAA Journal of Automatica Sinica.

selected publications

IOP-JNE

EEG-inception: an accurate and robust end-to-end neural network for EEG-based motor imagery classification

Zhang, Ce, Kim, Young-Keun, and Eskandarian, Azim

Journal of Neural Engineering Mar 2021

Abs arXiv HTML

Objective. Classification of electroencephalography (EEG)-based motor imagery (MI) is a crucial non-invasive application in brain–computer interface (BCI) research. This paper proposes a novel convolutional neural network (CNN) architecture for accurate and robust EEG-based MI classification that outperforms the state-of-the-art methods. Approach. The proposed CNN model, namely EEG-inception, is built on the backbone of the inception-time network, which has showed to be highly efficient and accurate for time-series classification. Also, the proposed network is an end-to-end classification, as it takes the raw EEG signals as the input and does not require complex EEG signal-preprocessing. Furthermore, this paper proposes a novel data augmentation method for EEG signals to enhance the accuracy, at least by 3%, and reduce overfitting with limited BCI datasets. Main results. The proposed model outperforms all state-of-the-art methods by achieving the average accuracy of 88.4% and 88.6% on the 2008 BCI Competition IV 2a (four-classes) and 2b datasets (binary-classes), respectively. Furthermore, it takes less than 0.025 s to test a sample suitable for real-time processing. Moreover, the classification standard deviation for nine different subjects achieves the lowest value of 5.5 for the 2b dataset and 7.1 for the 2a dataset, which validates that the proposed method is highly robust. Significance. From the experiment results, it can be inferred that the EEG-inception network exhibits a strong potential as a subject-independent classifier for EEG-based MI tasks.
IEEE/CAA-JAS

A Survey and Tutorial of EEG-Based Brain Monitoring for Driver State Analysis

Zhang, Ce, and Eskandarian, Azim

IEEE/CAA Journal of Automatica Sinica Mar 2021

Abs arXiv HTML

The driver’s cognitive and physiological states affect his/her ability to control the vehicle. Thus, these driver states are essential to the safety of automobiles. The design of advanced driver assistance systems (ADAS) or autonomous vehicles will depend on their ability to interact effectively with the driver. A deeper understanding of the driver state is, therefore, paramount. Electroencephalography (EEG) is proven to be one of the most effective methods for driver state monitoring and human error detection. This paper discusses EEG-based driver state detection systems and their corresponding analysis algorithms over the last three decades. First, the commonly used EEG system setup for driver state studies is introduced. Then, the EEG signal preprocessing, feature extraction, and classification algorithms for driver state detection are reviewed. Finally, EEG-based driver state monitoring research is reviewed in-depth, and its future development is discussed. It is concluded that the current EEG-based driver state monitoring algorithms are promising for safety applications. However, many improvements are still required in EEG artifact reduction, real-time processing, and between-subject classification accuracy.
IEEE-ITSC

Attention-based Neural Network for Driving Environment Complexity Perception (Best Student Paper Award)

Zhang, Ce, Eskandarian, Azim, and Du, Xuelai

In 2021 IEEE International Intelligent Transportation Systems Conference (ITSC) Mar 2021

Abs arXiv HTML

The driver’s cognitive and physiological states affect his/her ability to control the vehicle. Thus, these driver states are essential to the safety of automobiles. The design of advanced driver assistance systems (ADAS) or autonomous vehicles will depend on their ability to interact effectively with the driver. A deeper understanding of the driver state is, therefore, paramount. Electroencephalography (EEG) is proven to be one of the most effective methods for driver state monitoring and human error detection. This paper discusses EEG-based driver state detection systems and their corresponding analysis algorithms over the last three decades. First, the commonly used EEG system setup for driver state studies is introduced. Then, the EEG signal preprocessing, feature extraction, and classification algorithms for driver state detection are reviewed. Finally, EEG-based driver state monitoring research is reviewed in-depth, and its future development is discussed. It is concluded that the current EEG-based driver state monitoring algorithms are promising for safety applications. However, many improvements are still required in EEG artifact reduction, real-time processing, and between-subject classification accuracy.