Filter bank temporally local canonical correlation analysis for short time window SSVEPs classification
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RESEARCH ARTICLE
Filter bank temporally local canonical correlation analysis for short time window SSVEPs classification Xinghan Shao1 • Mingxing Lin1 Received: 25 February 2020 / Revised: 21 June 2020 / Accepted: 19 July 2020 Ó Springer Nature B.V. 2020
Abstract Canonical correlation analysis (CCA) method and its extended methods have been widely and successfully applied to the frequency recognition in SSVEP-based BCI systems. As a state-of-the-art extended method, filter bank canonical correlation analysis has higher accuracy and information transmission rate (ITR) than CCA. However, in the CCA method, the temporally local structure of samples has not been well considered. In this correspondence, we proposed termed temporally local canonical correlation analysis (TCCA). In this new method, the original covariance matrix was replaced by the temporally local covariance matrix. Furthermore, we proposed an improved frequency identification method of filter bank based on TCCA, named filter bank temporally local canonical correlation analysis (FBTCCA). In the offline environment, we used a leave-one-subject-out validation strategy on datasets of ten testees to optimize the parameters of TCCA and FBTCCA and evaluate the two algorithms. The experimental results affirm that TCCA markedly outperformed CCA, and FBTCCA obtained the highest accuracy among the four methods. This study corroborates that TCCA-based approaches have great potential for implementing short time window SSVEP-based BCI systems. Keywords Brain–computer interface (BCI) Steady-state visual evoked potential (SSVEP) Canonical correlation analysis (CCA) Temporally local information
Introduction Brain–computer interface (BCI) is an emerging method that could provide a new communication channel between a human and an external environment (Lance et al. 2012; Chaudhary et al. 2016). In a BCI system, noninvasive EEG has been valued by researchers because of its safety and low-cost devices (Lay-Ekuakille et al. 2013; Dai et al. 2015; Chen et al. 2016; Wang et al. 2016). There are several brain control signals commonly used in BCI systems, such as event-related potential (ERP) (Cheng et al. 2002; Gao et al. 2014; Miao et al. 2020), sensorimotor rhythm (SMR) (He et al. 2015; Zhang et al. 2017; Feng et al. 2018), auditory steady-state response (ASSR) & Mingxing Lin [email protected] Xinghan Shao [email protected] 1
School of Mechanical Engineering, Shandong University, Jinan 250000, China
(Hwang et al. 2017),steady-state visual evoked potential (SSVEP) (Yin et al. 2014; Chen et al. 2015b; Zhang et al. 2015; Chang et al. 2016; Jiao et al. 2018), etc. The SSVEP-based BCI is one of the core areas of research in the field of BCI, and studies indicated that it has the preponderance of high information transmission rate and less user training (Cheng et al. 2002; Wang et al. 2008; Bakardjian et al. 2010; Gao et al. 2014). SSVEPs are generated by the users staring at the visual flickers and include the same frequenc
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