Abstract
Event-related potentials (ERP) based brain-computer interfaces (BCI) is a promising technology for decoding mental states. Due to the high trail-to-trial variability and low signal-to-noise ratio caused by volume conduction, analyzing brain states corresponding to ERP on a single trial is a challenging task. In this paper, we propose a computationally efficient method for ERP feature extraction, termed spatial filtering and temporal down-sampling (SFTDS). The spatial filters and the temporal down-sampling weight vectors can be optimized under a single objective function by SFTDS. Experiments on real P300 data from 10 subjects show the superiority of SFTDS over other algorithms.
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Acknowledgments
This work was supported by 973 Program of China (No. 2015CB351703), the National Natural Science Foundation of China (No. 61403144, No. 61633010), the tip-top Scientific and Technical Innovative Youth Talents of Guangdong special support program (No. 2015TQ01X361).
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Qi, F., Li, Y., Wen, Z., Wu, W. (2017). An Algorithm Combining Spatial Filtering and Temporal Down-Sampling with Applications to ERP Feature Extraction. In: Liu, D., Xie, S., Li, Y., Zhao, D., El-Alfy, ES. (eds) Neural Information Processing. ICONIP 2017. Lecture Notes in Computer Science(), vol 10635. Springer, Cham. https://doi.org/10.1007/978-3-319-70096-0_75
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DOI: https://doi.org/10.1007/978-3-319-70096-0_75
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