Abstract
The application of biometric identification technology has been applied extensively in modern society. EEG pattern recognition method is one of the key biometric identification technologies for advanced secure and reliable identification technology. This paper introduces a novel EEG pattern recognition method based on Segmented EEG Graph using PLA (SEGPA) model, which incorporates the novel self-adjusting time series dependency method. In such a model, the dynamic time-dependency method has been applied in the recognition process. The preliminary experimental results indicate that the proposed method can produce a reasonable recognition outcome.
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Acknowledgement
This work is partially supported by Zhejiang Natural Science Fund (LY19F030010), Ningbo Innovation Team (No. 2016C11024), National Natural Science Fund of China (No. 61572022).
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Zhang, H.L., Xue, Y., Zhang, B., Li, X., Lu, X. (2020). EEG Pattern Recognition Based on Self-adjusting Dynamic Time Dependency Method. In: He, J., et al. Data Science. ICDS 2019. Communications in Computer and Information Science, vol 1179. Springer, Singapore. https://doi.org/10.1007/978-981-15-2810-1_31
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DOI: https://doi.org/10.1007/978-981-15-2810-1_31
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