A dynamic submatrix-based P300 online brain–computer interface. (January 2015)

• Record Type: Journal Article
• Authors: Shen, Jizhong; Liang, Jianwei; Shi, Jinhe; Wang, Yupeng
• Is Part Of: Biomedical signal processing and control. Volume 15(2015)
• Page Start: 27
• Page End: 32
• Publication Date: 2015-01
• Subjects: Brain–computer interface -- Submatrix-based paradigm -- Dynamic algorithms -- Online -- P300; Signal processing -- Periodicals; Biomedical engineering -- Periodicals; Signal Processing, Computer-Assisted -- Periodicals; Image Processing, Computer-Assisted -- Periodicals; Biomedical Engineering -- Periodicals; 610.28
• DOI: 10.1016/j.bspc.2014.09.005
• Languages: English
• ISSNs: 1746-8094

Abstract

Highlights: A dynamic SBP online BCI is proposed and implemented in this paper. Two threshold algorithms are proposed and compared. The number of sequences is dynamically limited by the threshold of our algorithm. Compared with CBP and NFA, the results show significant improvement of practical ITR. Abstract: The P300 brain–computer interface (BCI) system relies on an oddball paradigm to elicit the P300. Besides the traditional row/column paradigm (RCP), many visual paradigms have been proposed for the P300 BCI. In our previous work, submatrix-based paradigm (SBP) was proposed and proven to be superior to the RCP in performance and user acceptability. To further improve the performance and realize an online BCI system, a dynamic SBP online BCI is proposed in this paper. The dynamic algorithm employs a threshold which dynamically limits the number of sequences. Based on analyzing the distribution characteristic of P300 in SBP, two threshold algorithms named the maximum value algorithm and the pseudo-kurtosis algorithm are proposed and compared. Online experimental results show that both dynamic algorithms can improve the performance of the SBP P300 speller, and the mean practical bit rate of the maximum value algorithm is 34.36 bits/min, which is 9.04% higher than that of checkerboard paradigm (CBP) and 11.2% higher than that of Jin's n-flash adaptive system (NFA).