Lateral Control of Brain-Controlled Vehicle Based on SVM Probability Output Model
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arXiv
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| Autores principales: | , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| _version_ | 1866914709035286528 |
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| author | Pan, Hongguang Yu, Xinyu Yang, Yong |
| author_facet | Pan, Hongguang Yu, Xinyu Yang, Yong |
| contents | The non-stationary characteristics of EEG signal and the individual differences of brain-computer interfaces (BCIs) lead to poor performance in the control process of the brain-controlled vehicles (BCVs). In this paper, by combining steady-state visual evoked potential (SSVEP) interactive interface, brain instructions generation module and vehicle lateral control module, a probabilistic output model based on support vector machine (SVM) is proposed for BCV lateral control to improve the driving performance. Firstly, a filter bank common spatial pattern (FBCSP) algorithm is introduced into the brain instructions generation module, which can improve the off-line decoding performance. Secondly, a sigmod-fitting SVM (SF-SVM) is trained based on the sigmod-fitting method and the lateral control module is developed, which can produce all commands in the form of probability instead of specific single command. Finally, a pre-experiment and two road-keeping experiments are conducted. In the pre-experiment, the experiment results show that, the average highest off-line accuracy among subjects is 95.64\%, while for those in the online stage, the average accuracy is only 84.44\%. In the road-keeping experiments, the task completion rate in the two designed scenes increased by 25.6\% and 20\%, respectively. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_05762 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Lateral Control of Brain-Controlled Vehicle Based on SVM Probability Output Model Pan, Hongguang Yu, Xinyu Yang, Yong Neurons and Cognition The non-stationary characteristics of EEG signal and the individual differences of brain-computer interfaces (BCIs) lead to poor performance in the control process of the brain-controlled vehicles (BCVs). In this paper, by combining steady-state visual evoked potential (SSVEP) interactive interface, brain instructions generation module and vehicle lateral control module, a probabilistic output model based on support vector machine (SVM) is proposed for BCV lateral control to improve the driving performance. Firstly, a filter bank common spatial pattern (FBCSP) algorithm is introduced into the brain instructions generation module, which can improve the off-line decoding performance. Secondly, a sigmod-fitting SVM (SF-SVM) is trained based on the sigmod-fitting method and the lateral control module is developed, which can produce all commands in the form of probability instead of specific single command. Finally, a pre-experiment and two road-keeping experiments are conducted. In the pre-experiment, the experiment results show that, the average highest off-line accuracy among subjects is 95.64\%, while for those in the online stage, the average accuracy is only 84.44\%. In the road-keeping experiments, the task completion rate in the two designed scenes increased by 25.6\% and 20\%, respectively. |
| title | Lateral Control of Brain-Controlled Vehicle Based on SVM Probability Output Model |
| topic | Neurons and Cognition |
| url | https://arxiv.org/abs/2403.05762 |