Multivariate weighted recurrent network for analyzing SSMVEP signals from EEG literate and illiterate
| Autor: | Yu-Xuan Yang, Chen Qu, Xin-Jun Zhou, Na Dong, Weidong Dang, Zhong-Ke Gao |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
Multivariate statistics
medicine.diagnostic_test Computer science business.industry General Physics and Astronomy Pattern recognition Cognition Electroencephalography 01 natural sciences 010305 fluids & plasmas Support vector machine 0103 physical sciences medicine Artificial intelligence Evoked potential 010306 general physics Canonical correlation business Clustering coefficient Brain–computer interface |
| Zdroj: | EPL (Europhysics Letters). 127:40004 |
| ISSN: | 1286-4854 |
| DOI: | 10.1209/0295-5075/127/40004 |
| Popis: | Recently, the Steady-State Motion Visual Evoked Potential (SSMVEP)-based Brain Computer Interface (BCI) has attracted a lot of attention. We design a SSMVEP-based BCI, in which a ring-shaped motion checkerboard pattern is used to realize SSMVEP stimulation. In particular, we firstly conduct SSMVEP experiments to obtain electroencephalogram (EEG) signals from 10 subjects, including 5 EEG literates and 5 EEG illiterates. By using the Canonical Correlation Analysis (CCA) and Support Vector Machine (SVM) method, we find that the classification accuracies of EEG illiterates are relatively lower than that of EEG literates. Thus, in order to investigate the differences in brain cognitive processes between the two groups of subjects, we construct a multivariate weighted recurrence network and analyze the weighted local efficiency and the clustering coefficient of the two groups. The results indicate that in SSMVEP experiment, there are significant differences between the two groups of subjects in these two network indicators. Our approach and analysis provide novel insights into the cognitive behavior of the brain and understanding of the “BCI Illiteracy” problem. |
| Databáze: | OpenAIRE |
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