SSVEP-Based Brain-Computer Interface with a Limited Number of Frequencies Based on Dual-Frequency Biased Coding

Sheng Ge, Yichuan Jiang, Mingming Zhang, Ruimin Wang, Keiji Iramina, Pan Lin, Yue Leng, Haixian Wang, Wenming Zheng

研究成果: ジャーナルへの寄稿学術誌査読

8 被引用数 (Scopus)

抄録

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.

本文言語英語
論文番号9404209
ページ(範囲)760-769
ページ数10
ジャーナルIEEE Transactions on Neural Systems and Rehabilitation Engineering
29
DOI
出版ステータス出版済み - 2021

!!!All Science Journal Classification (ASJC) codes

  • 内科学
  • 神経科学(全般)
  • 生体医工学
  • リハビリテーション

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