Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/137268
Title: Spectrum and phase adaptive CCA for SSVEP-based brain computer interface
Authors: Zhang, Zhuo
Wang, Chuanchu
Ang, Kai Keng
Wai, Aung Aung Phyo
Guan, Cuntai
Keywords: Engineering::Computer science and engineering
Issue Date: 2018
Source: Zhang, Z., Wang, C., Ang, K. K., Wai, A. A. P., & Guan, C. (2018). Spectrum and phase adaptive CCA for SSVEP-based brain computer interface. 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 311-314. doi:10.1109/EMBC.2018.8512267
Abstract: Among various brain activity patterns, Steady State Visual Evoked Potential (SSVEP) based Brain Computer Inter-face (BCI) requires the least training time while carrying the fastest information transfer rate, making it highly suitable for deploying efficient self-paced BCI systems. In this study, we propose a Spectrum and Phase Adaptive CCA (SPACCA) for subject-and device-specific SSVEP-based BCI. Cross subject heterogeneity of spectrum distribution is taken into consideration to improve the prediction accuracy. We design a library of phase shifting reference signals to accommodate subjective and device-related response time lag. With the flexible reference signal generating approach, the system can be optimized for any specific flickering source, include LED, computer screen and mobile devices. We evaluated the performance of SPACCA using three sets of data that use LED, computer screen and mobile device (tablet) as stimuli sources respectively. The first two data sets are publicly available whereas the third data set is collected in our BCI lab. Across different data sets, SPACCA consistently performs better than the baseline, i.e. standard CCA approach. Statistical test to compare the overall results across three data sets yield a p-value of 1.66e-6, implying the improvement is significant.
URI: https://hdl.handle.net/10356/137268
ISBN: 9781538636466
DOI: 10.1109/EMBC.2018.8512267
Rights: © 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The published version is available at: https://doi.org/10.1109/EMBC.2018.8512267.
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:SCSE Conference Papers

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