TY - GEN
T1 - Enhance Detection of SSVEPs through a Sinusoidal-Referenced Task-Related Component Analysis Method
AU - Wang, Zhenyu
AU - Xu, Tianheng
AU - Chen, Xianfu
AU - Zhou, Ting
AU - Hu, Honglin
AU - Wu, Celimuge
N1 - Funding Information:
This work was supported by the Science and Technology Commission Foundation of Shanghai (Nos. 21JM0010200 and 22xtcx00400), the Shanghai Pilot Program for Basic Research-Chinese Academy of Sciences, Shanghai Branch (No. JCYJ-SHFY-2022-0xx), the Shanghai Sailing Program (No. 22YF1454700), and the Young Elite Scientists Sponsorship Program by CIC (No. 2021QNRC001).
Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - The brain-computer interface (BCI) technology is deemed a pivotal technology in future wireless communication systems, e.g. 6G, for its capability to connect a brain and a machine. The device, paradigm, and algorithm are three most important aspects of a practical BCI. Among them, the detection algorithm has a decisive impact on the efficiency and robustness of the system. Great potential of artificial intelligence (AI) for decoding brains signals is also revealed. In this paper, we propose a new detection algorithm for the steady-state visual-evoked potential (SSVEP) based BCI, which is a typical noninvasive BCI paradigm and achieves by far the highest information transfer rate (ITR) among various noninvasive systems. The new algorithm is termed sinusoidal-referenced task-related component analysis (srTRCA). It resembles conventional algorithms like TRCA in the way that it is also based on spatial filtering and template matching. However, compared with conventional algorithms like TRCA, srTRCA makes better use of the prior knowledge of the SSVEP signal being sinusoidal. By introducing a new item which characterizes the correlation between the task-related component and sinusoidal reference to its objective function, srTRCA is expected to achieve an enhanced detection performance, especially in the situation where training is insufficient. The performance of srTRCA is tested on a benchmark SSVEP dataset which includes 35 subjects. Three algorithms are taken as baselines with them being canonical correlation analysis (CCA), TRCA, and similarity-constrained TRCA (scTRCA). Results show that srTRCA achieves a fair performance enhancement compared with three baselines. The validity of the proposed srTRCA algorithm is proved.
AB - The brain-computer interface (BCI) technology is deemed a pivotal technology in future wireless communication systems, e.g. 6G, for its capability to connect a brain and a machine. The device, paradigm, and algorithm are three most important aspects of a practical BCI. Among them, the detection algorithm has a decisive impact on the efficiency and robustness of the system. Great potential of artificial intelligence (AI) for decoding brains signals is also revealed. In this paper, we propose a new detection algorithm for the steady-state visual-evoked potential (SSVEP) based BCI, which is a typical noninvasive BCI paradigm and achieves by far the highest information transfer rate (ITR) among various noninvasive systems. The new algorithm is termed sinusoidal-referenced task-related component analysis (srTRCA). It resembles conventional algorithms like TRCA in the way that it is also based on spatial filtering and template matching. However, compared with conventional algorithms like TRCA, srTRCA makes better use of the prior knowledge of the SSVEP signal being sinusoidal. By introducing a new item which characterizes the correlation between the task-related component and sinusoidal reference to its objective function, srTRCA is expected to achieve an enhanced detection performance, especially in the situation where training is insufficient. The performance of srTRCA is tested on a benchmark SSVEP dataset which includes 35 subjects. Three algorithms are taken as baselines with them being canonical correlation analysis (CCA), TRCA, and similarity-constrained TRCA (scTRCA). Results show that srTRCA achieves a fair performance enhancement compared with three baselines. The validity of the proposed srTRCA algorithm is proved.
UR - https://www.scopus.com/pages/publications/85171623838
U2 - 10.1109/INFOCOMWKSHPS57453.2023.10226001
DO - 10.1109/INFOCOMWKSHPS57453.2023.10226001
M3 - Conference article in proceedings
AN - SCOPUS:85171623838
SN - 978-1-6654-9428-1
T3 - IEEE Conference on Computer Communications workshops
BT - IEEE INFOCOM 2023 - Conference on Computer Communications Workshops, INFOCOM WKSHPS 2023
PB - IEEE Institute of Electrical and Electronic Engineers
T2 - IEEE INFOCOM Conference on Computer Communications Workshops, INFOCOM WKSHPS 2023
Y2 - 20 May 2023 through 20 May 2023
ER -