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A transplantation of subject-independent model in cross-platform BCI

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Abstract

With the development of wearable technology, portable wireless systems have been used gradually for collecting electroencephalogram (EEG) signals. However, the introduction of portable collection devices always means a descent in signal-to-noise ratio (SNR) of EEG. Subject-independent brain-computer interface (BCI) avoids conventional calibration procedure for new users. However, whether subject-independent model can be used in cross-platform BCI has not been discussed so far. This paper transplanted the subject-independent model from a high-SNR platform to a lower one for recognition in P300-Speller. After comparing their EEG features elicited from diverse collection platforms, a model adjustment strategy was proposed to increase recognition accuracy. By model adjustment, the average accuracy was 85.00% in online spell experiments. The results indicate it is feasible for subject-independent model transplantation, especially after model adjustment strategy. It provides technology supported for further development of cross-platform BCI.

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Acknowledgements

Research is supported by National Natural Science Foundation of China (No. 91520205, 81222021, 31271062, 31500865, 81571762, 61172008, 81171423, 30970875, 90920015), National Key Technology R&D Program of the Ministry of Science and Technology of China (No.2012BAI34B02), Tianjin Key Technology R&D Program (No. 15ZCZDSY00930, 13JCQNJC13900, 15JCYBJC29600) and Program for New Century Excellent Talents in University of the Ministry of Education of China (No. NCET-10-0618).

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    Authors and Affiliations

    1. Department of Biomedical Engineering, College of Precision Instruments and Optoelectronics Engineering, Tianjin University, Tianjin, China

      Yawei Zhao, Zhongpeng Wang, Jing Liu, Long Chen, Hongzhi Qi, Feng He, Peng Zhou & Dong Ming

    2. National Key Laboratory of Human Factors Engineering, China Astronaut Research and Training Center, Beijing, China

      Zhen Zhang & Xuejun Jiao

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    1. Yawei Zhao
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    2. Zhongpeng Wang
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    Correspondence to Hongzhi Qi.

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    Z. Zhang contributed equally to this work.

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    Zhao, Y., Wang, Z., Zhang, Z. et al. A transplantation of subject-independent model in cross-platform BCI. Int. J. Mach. Learn. & Cyber. 9, 959–967 (2018). https://doi.org/10.1007/s13042-016-0620-1

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