A Driving Performance Forecasting System Based on Brain Dynamic State Analysis Using 4-D Convolutional Neural Networks
| Autor: | Yu-Kai Wang, Chieh-Ning Fang, Yu-Chia Hung, Dongrui Wu, Chin-Teng Lin, Chun-Hsiang Chuang |
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| Rok vydání: | 2021 |
| Předmět: |
Computer science
0206 medical engineering Feature extraction Word error rate 02 engineering and technology Electroencephalography Convolutional neural network 03 medical and health sciences 0302 clinical medicine medicine Humans Electrical and Electronic Engineering medicine.diagnostic_test business.industry Deep learning Brain Pattern recognition Cognition Human brain 020601 biomedical engineering Computer Science Applications Human-Computer Interaction medicine.anatomical_structure Control and Systems Engineering Task analysis Neural Networks Computer Artificial intelligence business Algorithms 030217 neurology & neurosurgery Software Information Systems |
| Zdroj: | IEEE Transactions on Cybernetics. 51:4959-4967 |
| ISSN: | 2168-2275 2168-2267 |
| Popis: | Vehicle accidents are the primary cause of fatalities worldwide. Most often, experiencing fatigue on the road leads to operator errors and behavioral lapses. Thus, there is a need to predict the cognitive state of drivers, particularly their fatigue level. Electroencephalography (EEG) has been demonstrated to be effective for monitoring changes in the human brain state and behavior. Thirty-seven subjects participated in this driving experiment and performed a perform lane-keeping task in a visual-reality environment. Three domains, namely, frequency, temporal, and 2-D spatial information, of the EEG channel location were comprehensively considered. A 4-D convolutional neural-network (4-D CNN) algorithm was then proposed to associate all information from the EEG signals and the changes in the human state and behavioral performance. A 4-D CNN achieves superior forecasting performance over 2-D CNN, 3-D CNN, and shallow networks. The results showed a 3.82% improvement in the root mean-square error, a 3.45% improvement in the error rate, and a 11.98% improvement in the correlation coefficient with 4-D CNN compared with 3-D CNN. The 4-D CNN algorithm extracts the significant theta and alpha activations in the frontal and posterior cingulate cortices under distinct fatigue levels. This work contributes to enhancing our understanding of deep learning methods in the analysis of EEG signals. We even envision that deep learning might serve as a bridge between translation neuroscience and further real-world applications. |
| Databáze: | OpenAIRE |
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