TY - JOUR
T1 - SSVEP-Based Brain-Computer Interface with a Limited Number of Frequencies Based on Dual-Frequency Biased Coding
AU - Ge, Sheng
AU - Jiang, Yichuan
AU - Zhang, Mingming
AU - Wang, Ruimin
AU - Iramina, Keiji
AU - Lin, Pan
AU - Leng, Yue
AU - Wang, Haixian
AU - Zheng, Wenming
N1 - Funding Information:
Manuscript received November 27, 2020; revised February 9, 2021 and April 2, 2021; accepted April 5, 2021. Date of publication April 14, 2021; date of current version April 28, 2021. This work was supported in part by the National Key Research and Development Program of China under Grant 2018YFB1305200, in part by the Guangdong Basic and Applied Basic Research Foundation under Grant 2020A1515010401, and in part by the National Natural Science Foundation of China under Grants 61921004, 62071177, and 62076064. (Sheng Ge and Yichuan Jiang are co-first authors.) (Corresponding authors: Pan Lin; Wenming Zheng.) This work involved human subjects or animals in its research. Approval of all ethical and experimental procedures and protocols was granted by the Ethics Committee of Affiliated Zhongda Hospital, Southeast University, under Approval Nos. 2016ZDSYLL002.0 and 2016ZDSYLL002-Y01, and performed in line with the Declaration of Helsinki.
Publisher Copyright:
© 2001-2011 IEEE.
PY - 2021
Y1 - 2021
N2 - How to encode as many targets as possible with a limited-frequency resource is a difficult problem in the practical use of a steady-state visual evoked potential (SSVEP) based brain-computer interface (BCI) speller. To solve this problem, this study developed a novel method called dual-frequency biased coding (DFBC) to tag targets in a SSVEP-based 48-character virtual speller, in which each target is encoded with a permutation sequence consisting of two permuted flickering periods that flash at different frequencies. The proposed paradigm was validated by 11 participants in an offline experiment and 7 participants in an online experiment. Three occipital channels (O1, Oz, and O2) were used to obtain the SSVEP signals for identifying the targets. Based on the coding characteristics of the DFBC method, the proposed approach has the ability of self-correction and thus achieves an accuracy of 76.6% and 79.3% for offline and online experiments, respectively, which outperforms the traditional multiple frequencies sequential coding (MFSC) method. This study demonstrates that DFBC is an efficient method for coding a high number of SSVEP targets with a small number of available frequencies.
AB - How to encode as many targets as possible with a limited-frequency resource is a difficult problem in the practical use of a steady-state visual evoked potential (SSVEP) based brain-computer interface (BCI) speller. To solve this problem, this study developed a novel method called dual-frequency biased coding (DFBC) to tag targets in a SSVEP-based 48-character virtual speller, in which each target is encoded with a permutation sequence consisting of two permuted flickering periods that flash at different frequencies. The proposed paradigm was validated by 11 participants in an offline experiment and 7 participants in an online experiment. Three occipital channels (O1, Oz, and O2) were used to obtain the SSVEP signals for identifying the targets. Based on the coding characteristics of the DFBC method, the proposed approach has the ability of self-correction and thus achieves an accuracy of 76.6% and 79.3% for offline and online experiments, respectively, which outperforms the traditional multiple frequencies sequential coding (MFSC) method. This study demonstrates that DFBC is an efficient method for coding a high number of SSVEP targets with a small number of available frequencies.
UR - http://www.scopus.com/inward/record.url?scp=85104261300&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85104261300&partnerID=8YFLogxK
U2 - 10.1109/TNSRE.2021.3073134
DO - 10.1109/TNSRE.2021.3073134
M3 - Article
C2 - 33852388
AN - SCOPUS:85104261300
SN - 1534-4320
VL - 29
SP - 760
EP - 769
JO - IEEE Transactions on Neural Systems and Rehabilitation Engineering
JF - IEEE Transactions on Neural Systems and Rehabilitation Engineering
M1 - 9404209
ER -