An inter-subject model to reduce the calibration time for motion imagination-based brain-computer interface.
A major factor blocking the practical application of brain-computer interfaces (BCI) is the long calibration time. To obtain enough training trials, participants must spend a long time in the calibration stage. In this paper, we propose a new framework to reduce the calibration time through knowledg...
| Publicado en: | Medical & Biological Engineering & Computing Vol. 57; no. 4; pp. 939 - 953 |
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| Autores principales: | , , , , , |
| Formato: | Journal Article |
| Publicado: |
Springer Nature
Apr2019
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=135753228&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 135753228 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01400118 PO0 jtl: Medical & Biological Engineering & Computing issn: 01400118 maglogo: N pubinfo: dt: Apr2019 vid: 57 iid: 4 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 135753228 135753228 NLM30498878 10.1007/s11517-018-1917-x NLM30498878 135753228 ppf: 939 ppct: 14 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: An inter-subject model to reduce the calibration time for motion imagination-based brain-computer interface. aug: au: Zou, Yijun Zhao, Xingang Chu, Yaqi Zhao, Yiwen Xu, Weiliang Han, Jianda affil: University of Chinese Academy of Sciences, 100049, Beijing, China sug: subj: Imagination Brain-Computer Interfaces Models, Biological Motion Calibration Time Factors Algorithms ab: A major factor blocking the practical application of brain-computer interfaces (BCI) is the long calibration time. To obtain enough training trials, participants must spend a long time in the calibration stage. In this paper, we propose a new framework to reduce the calibration time through knowledge transferred from the electroencephalogram (EEG) of other subjects. We trained the motor recognition model for the target subject using both the target's EEG signal and the EEG signals of other subjects. To reduce the individual variation of different datasets, we proposed two data mapping methods. These two methods separately diminished the variation caused by dissimilarities in the brain activation region and the strength of the brain activation in different subjects. After these data mapping stages, we adopted an ensemble method to aggregate the EEG signals from all subjects into a final model. We compared our method with other methods that reduce the calibration time. The results showed that our method achieves a satisfactory recognition accuracy using very few training trials (32 samples). Compared with existing methods using few training trials, our method achieved much greater accuracy. Graphical abstract The framework of the proposed method. The workflow of the framework have three steps: 1, process each subjects EEG signals according to the target subject's EEG signal. 2, generate models from each subjects' processed signals. 3, ensemble these models to a final model, the final model is a model for the target subject. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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