论文信息 - Using Noninvasive Neural Signal to Recognize Single- and Multi-task States of Operators

Using Noninvasive Neural Signal to Recognize Single- and Multi-task States of Operators

In this paper, we propose an electroencephalogram (EEG) signals-based method to recognize single- and multi-task states of users by using the linear discriminant analysis (LDA) algorithm and convolutional neural network (CNN). A novel experimental paradigm is designed to validate the proposed method. Experimental results from eight subjects show that the proposed methods perform well. Furthermore, the average accuracy of the recognition model based on CNN reaches 89.13% and is 5% higher than that based on the LDA algorithm. This work not only lays a foundation for the development of adaptive assistant systems based on brain-computer interfaces, but it also advances the study of human state monitoring and human-machine interaction based on EEG signals.

Luzheng Bi | Xiaoguang Wang | Shengchao Xia

[1] L. Naccache,et al. Disentangling conscious from unconscious cognitive processing with event-related EEG potentials. , 2017, Revue neurologique.

[2] Luzheng Bi,et al. EEG-based Universal Prediction Model of Emergency Braking Intention for Brain-controlled Vehicles* , 2019, 2019 9th International IEEE/EMBS Conference on Neural Engineering (NER).

[3] Bin He,et al. Brain–Computer Interfaces Using Sensorimotor Rhythms: Current State and Future Perspectives , 2014, IEEE Transactions on Biomedical Engineering.

[4] C. Neuper,et al. Combining Brain–Computer Interfaces and Assistive Technologies: State-of-the-Art and Challenges , 2010, Front. Neurosci..

[5] Cuntai Guan,et al. A Novel Method of Emergency Situation Detection for a Brain-Controlled Vehicle by Combining EEG Signals With Surrounding Information , 2018, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[6] Chris Yakopcic,et al. A State-of-the-Art Survey on Deep Learning Theory and Architectures , 2019, Electronics.

[7] Yili Liu,et al. A Brain–Computer Interface-Based Vehicle Destination Selection System Using P300 and SSVEP Signals , 2015, IEEE Transactions on Intelligent Transportation Systems.

[8] Luzheng Bi,et al. EEG-Based Detection of Driver Emergency Braking Intention for Brain-Controlled Vehicles , 2018, IEEE Transactions on Intelligent Transportation Systems.