A novel strategy for driving car brain–computer interfaces: Discrimination of EEG-based visual-motor imagery

A brain–computer interface (BCI) based on kinesthetic motor imagery has a potential of becoming a groundbreaking technology in a clinical setting. However, few studies focus on a visual-motor imagery (VMI) paradigm driving BCI. The VMI-BCI feature extraction methods are yet to be explored in depth....

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Autores principales:	Zhou Zhouzhou, Gong Anmin, Qian Qian, Su Lei, Zhao Lei, Fu Yunfa
Formato:	article
Lenguaje:	EN
Publicado:	De Gruyter 2021
Materias:	visual-motor imagery hilbert–huang transformation eeg brain–computer interface svm Neurosciences. Biological psychiatry. Neuropsychiatry RC321-571
Acceso en línea:	https://doaj.org/article/9a7ef098028646d5a15a947939ba66ec
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spelling	oai:doaj.org-article:9a7ef098028646d5a15a947939ba66ec2021-12-05T14:11:05ZA novel strategy for driving car brain–computer interfaces: Discrimination of EEG-based visual-motor imagery2081-693610.1515/tnsci-2020-0199https://doaj.org/article/9a7ef098028646d5a15a947939ba66ec2021-11-01T00:00:00Zhttps://doi.org/10.1515/tnsci-2020-0199https://doaj.org/toc/2081-6936A brain–computer interface (BCI) based on kinesthetic motor imagery has a potential of becoming a groundbreaking technology in a clinical setting. However, few studies focus on a visual-motor imagery (VMI) paradigm driving BCI. The VMI-BCI feature extraction methods are yet to be explored in depth. In this study, a novel VMI-BCI paradigm is proposed to execute four VMI tasks: imagining a car moving forward, reversing, turning left, and turning right. These mental strategies can naturally control a car or robot to move forward, backward, left, and right. Electroencephalogram (EEG) data from 25 subjects were collected. After the raw EEG signal baseline was corrected, the alpha band was extracted using bandpass filtering. The artifacts were removed by independent component analysis. Then, the EEG average instantaneous energy induced by VMI (VMI-EEG) was calculated using the Hilbert–Huang transform (HHT). The autoregressive model was extracted to construct a 12-dimensional feature vector to a support vector machine suitable for small sample classification. This was classified into two-class tasks: visual imagination of driving the car forward versus reversing, driving forward versus turning left, driving forward versus turning right, reversing versus turning left, reversing versus turning right, and turning left versus turning right. The results showed that the average classification accuracy of these two-class tasks was 62.68 ± 5.08%, and the highest classification accuracy was 73.66 ± 6.80%. The study showed that EEG features of O1 and O2 electrodes in the occipital region extracted by HHT were separable for these VMI tasks.Zhou ZhouzhouGong AnminQian QianSu LeiZhao LeiFu YunfaDe Gruyterarticlevisual-motor imageryhilbert–huang transformationeegbrain–computer interfacesvmNeurosciences. Biological psychiatry. NeuropsychiatryRC321-571ENTranslational Neuroscience, Vol 12, Iss 1, Pp 482-493 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	visual-motor imagery hilbert–huang transformation eeg brain–computer interface svm Neurosciences. Biological psychiatry. Neuropsychiatry RC321-571
spellingShingle	visual-motor imagery hilbert–huang transformation eeg brain–computer interface svm Neurosciences. Biological psychiatry. Neuropsychiatry RC321-571 Zhou Zhouzhou Gong Anmin Qian Qian Su Lei Zhao Lei Fu Yunfa A novel strategy for driving car brain–computer interfaces: Discrimination of EEG-based visual-motor imagery
description	A brain–computer interface (BCI) based on kinesthetic motor imagery has a potential of becoming a groundbreaking technology in a clinical setting. However, few studies focus on a visual-motor imagery (VMI) paradigm driving BCI. The VMI-BCI feature extraction methods are yet to be explored in depth. In this study, a novel VMI-BCI paradigm is proposed to execute four VMI tasks: imagining a car moving forward, reversing, turning left, and turning right. These mental strategies can naturally control a car or robot to move forward, backward, left, and right. Electroencephalogram (EEG) data from 25 subjects were collected. After the raw EEG signal baseline was corrected, the alpha band was extracted using bandpass filtering. The artifacts were removed by independent component analysis. Then, the EEG average instantaneous energy induced by VMI (VMI-EEG) was calculated using the Hilbert–Huang transform (HHT). The autoregressive model was extracted to construct a 12-dimensional feature vector to a support vector machine suitable for small sample classification. This was classified into two-class tasks: visual imagination of driving the car forward versus reversing, driving forward versus turning left, driving forward versus turning right, reversing versus turning left, reversing versus turning right, and turning left versus turning right. The results showed that the average classification accuracy of these two-class tasks was 62.68 ± 5.08%, and the highest classification accuracy was 73.66 ± 6.80%. The study showed that EEG features of O1 and O2 electrodes in the occipital region extracted by HHT were separable for these VMI tasks.
format	article
author	Zhou Zhouzhou Gong Anmin Qian Qian Su Lei Zhao Lei Fu Yunfa
author_facet	Zhou Zhouzhou Gong Anmin Qian Qian Su Lei Zhao Lei Fu Yunfa
author_sort	Zhou Zhouzhou
title	A novel strategy for driving car brain–computer interfaces: Discrimination of EEG-based visual-motor imagery
title_short	A novel strategy for driving car brain–computer interfaces: Discrimination of EEG-based visual-motor imagery
title_full	A novel strategy for driving car brain–computer interfaces: Discrimination of EEG-based visual-motor imagery
title_fullStr	A novel strategy for driving car brain–computer interfaces: Discrimination of EEG-based visual-motor imagery
title_full_unstemmed	A novel strategy for driving car brain–computer interfaces: Discrimination of EEG-based visual-motor imagery
title_sort	novel strategy for driving car brain–computer interfaces: discrimination of eeg-based visual-motor imagery
publisher	De Gruyter
publishDate	2021
url	https://doaj.org/article/9a7ef098028646d5a15a947939ba66ec
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A novel strategy for driving car brain–computer interfaces: Discrimination of EEG-based visual-motor imagery

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