Recognition of SSMVEP signals based on multi-channel integrated GT2circ statistic method
| Autor: | Xiaodong Zhang, Xingliang Han, Xiaoqi Mu, Min Li, Jun Xie, Ailing Luo, Guanghua Xu |
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| Rok vydání: | 2017 |
| Předmět: |
Modality (human–computer interaction)
medicine.diagnostic_test Computer science Interface (computing) Speech recognition 0206 medical engineering 02 engineering and technology Electroencephalography 020601 biomedical engineering Visualization 03 medical and health sciences 0302 clinical medicine medicine Evoked potential Canonical correlation 030217 neurology & neurosurgery Statistic Brain–computer interface |
| Zdroj: | URAI |
| DOI: | 10.1109/urai.2017.7992703 |
| Popis: | Brain-computer interface (BCI) is a modern useful tool of bypassing usual channels of muscle and peripheral nervous system to establish a direct connection between brain and external devices and to restore fundamental communication and control skills. Steady-state visual evoked potential (SSVEP), as one of the most popular EEG modality, has been widely used in BCI applications. For SSVEP BCI, the most challenging task is to effectively improve the accuracy, especially in minimum number of recording electrodes and short stimulation duration. In this study, a novel multi-channel integrated GT2 circ statistic method was proposed for the frequency recognition in a four-class steady-state motion visual evoked potential (SSMVEP)-based BCI. The proposed method was compared with the widely used canonical correlation analysis (CCA) and verified with three-channel EEG data from three healthy subjects. Results indicated that a higher recognition performance with shorter recording time and few electrodes can be achieved by using of this novel method rather than CCA method, making multi-channel integrated GT2 circ statistic a robust approach for the implementation of SSVEP BCIs. |
| Databáze: | OpenAIRE |
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