TY - GEN
T1 - SSVEP Transient Feature Extraction and Rapid Recognition Method Based on Bistable Stochastic Resonance∗
AU - Yao, Pulin
AU - Xu, Guanghua
AU - Han, Chengcheng
AU - Zhang, Sicong
AU - Luo, Ailing
AU - Zhang, Qing
N1 - Publisher Copyright:
© 2018 IEEE.
PY - 2018/10/26
Y1 - 2018/10/26
N2 - Steady-state Visual Evoked Potential, SSVEP), as the most commonly used communication paradigm for non-implantable Brain-Computer Interface (BCI), boasts the advantages of unnecessity of training, noise immunity and periodicity. The traditional SSVEP extraction methods can effectively identify the target frequency contained in original EEG, however, the required data length usually lasts a few seconds. In this paper, bistable stochastic resonance (BSR) is applied to SSVEP extraction. BSR is very sensitive to amplitude mutation and frequency fluctuation of the input signal, making the output difference can be used for the detection of the target frequency. The processing results illustrate that the proposed method not only has a high recognition accuracy, but also effectively shortens the recognition time, thus improving the calculating speed. Therefore, SSVEP extraction based on BSR has a higher information transmission rate (ITR), which is more suitable for the real-time BCI system.
AB - Steady-state Visual Evoked Potential, SSVEP), as the most commonly used communication paradigm for non-implantable Brain-Computer Interface (BCI), boasts the advantages of unnecessity of training, noise immunity and periodicity. The traditional SSVEP extraction methods can effectively identify the target frequency contained in original EEG, however, the required data length usually lasts a few seconds. In this paper, bistable stochastic resonance (BSR) is applied to SSVEP extraction. BSR is very sensitive to amplitude mutation and frequency fluctuation of the input signal, making the output difference can be used for the detection of the target frequency. The processing results illustrate that the proposed method not only has a high recognition accuracy, but also effectively shortens the recognition time, thus improving the calculating speed. Therefore, SSVEP extraction based on BSR has a higher information transmission rate (ITR), which is more suitable for the real-time BCI system.
UR - https://www.scopus.com/pages/publications/85056658269
U2 - 10.1109/EMBC.2018.8512506
DO - 10.1109/EMBC.2018.8512506
M3 - 会议稿件
C2 - 30440256
AN - SCOPUS:85056658269
T3 - Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS
SP - 1
EP - 4
BT - 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2018
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2018
Y2 - 18 July 2018 through 21 July 2018
ER -