Correlated Component Analysis for Enhancing the Performance of SSVEP-Based Brain-Computer Interface

Zhang, Yangsong; Guo, Daqing; Li, Fali; Yin, Erwei; Zhang, Yu; Li, Peiyang; Zhao, Qibin; Tanaka, Tomomi; Yao, Dezhong; Xu, Peng

doi:10.1109/tnsre.2018.2826541

Cited by 88 publications

(58 citation statements)

References 46 publications

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“…Application to brain-computer interfaces. The reproducibility-based approach such as TRCA and correlated component analysis has attracted recent attention in the field of brain computer interfaces [61][62][63][64] . Inherent non-stationarity and variability of EEG have been an impediment in developing robust and training-free BCIs 33,65 .…”

Section: Discussionmentioning

confidence: 99%

Group task-related component analysis (gTRCA): a multivariate method for inter-trial reproducibility and inter-subject similarity maximization for EEG data analysis

Tanaka

2020

Sci Rep

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Group task-related component analysis (gtRcA): a multivariate method for inter-trial reproducibility and inter-subject similarity maximization for eeG data analysis Hirokazu tanaka eeG is known to contain considerable inter-trial and inter-subject variability, which poses a challenge in any group-level EEG analyses. A true experimental effect must be reproducible even with variabilities in trials, sessions, and subjects. extracting components that are reproducible across trials and subjects benefits both understanding common mechanisms in neural processing of cognitive functions and building robust brain-computer interfaces. this study extends our previous method (task-related component analysis, tRcA) by maximizing not only trial-by-trial reproducibility within single subjects but also similarity across a group of subjects, hence referred to as group tRcA (gtRcA). the problem of maximizing reproducibility of time series across trials and subjects is formulated as a generalized eigenvalue problem. We applied gTRCA to EEG data recorded from 35 subjects during a steady-state visual-evoked potential (SSVEP) experiment. The results revealed: (1) The group-representative data computed by gtRcA showed higher and consistent spectral peaks than other conventional methods; (2) Scalp maps obtained by gTRCA showed estimated source locations consistently within the occipital lobe; And (3) the high-dimensional features extracted by gTRCA are consistently mapped to a lowdimensional space. We conclude that gTRCA offers a framework for group-level EEG data analysis and brain-computer interfaces alternative in complement to grand averaging. A major issue in subject-level and group-level EEG analysis is intra-subject and inter-subject variability across trials and sessions that originates from both endogenous factors and exogenous factors 1. Endogenous factors of intra-subject variability include includes artifacts such as blinking, psychophysiological fluctuations, and fatigue, and exogenous factors include a temporal change in electrode impedance and positions. Inter-subject variability includes anatomical differences across subjects such as head shapes, skull conductivity, and patterns of brain gyrification (i.e., folding of the cerebral cortex), in which genetic differences play a primary role. These factors of variability consist of effects of non-interest and conceal an effect of interest related to a task. To sum, EEG is dynamic and non-stationary across sessions and subjects 2 , and the variability within and across subjects obfuscates both subject-and group-level analysis. Trial-mean across subjects (grand mean) is a simple and robust solution to improve signal-to-noise ratio by averaging out effects of non-interest within the group. In addition to univariate averaging, several multivariate methods have been proposed to enhance an experimental effect, including PCA-based ERP analysis 3-11 , partial-least-squares analysis 12 , independent component analysis 13,14 (see more detail in Discussion). Here, we propose a multivar...

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Section: Discussionmentioning

confidence: 99%

Group task-related component analysis (gTRCA): a multivariate method for inter-trial reproducibility and inter-subject similarity maximization for EEG data analysis

Tanaka

2020

Sci Rep

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“…Additionally, the CORRCA methods can be implemented faster than CCA, and TSCORRCA can be implemented faster than CCAICT. CORRCA was firstly introduced for frequency detection in our previous study [41]. In that study, we mainly used COR-RCA to learn spatial filters with multiple blocks of individual training data.…”

Section: Discussionmentioning

confidence: 99%

Two-Stage Frequency Recognition Method Based on Correlated Component Analysis for SSVEP-Based BCI

Zhang

Yin

et al. 2018

IEEE Trans. Neural Syst. Rehabil. Eng.

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A canonical correlation analysis (CCA) is a state-of-the-art method for frequency recognition in steady-state visual evoked potential (SSVEP)-based brain-computer interface (BCI) systems. Various extended methods have been developed, and among such methods, a combination method of CCA and individual-template-based CCA has achieved the best performance. However, the CCA requires the canonical vectors to be orthogonal, which may not be a reasonable assumption for the EEG analysis. In this paper, we propose using the correlated component analysis (CORRCA) rather than CCA to implement frequency recognition. CORRCA can relax the constraint of canonical vectors in CCA and generate the same projection vector for two multichannel EEG signals. Furthermore, we propose a two-stage method based on the basic CORRCA method (termed TSCORRCA). Evaluated on a benchmark data set of 35 subjects, the experimental results demonstrate that CORRCA significantly outperformed CCA, and TSCORRCA obtained the best performance among the compared methods. This paper demonstrates that CORRCA-based methods have a great potential for implementing high-performance SSVEP-based BCI systems.

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“…FBCCA was often combined with current innovative methods in [85], [101], thereby further optimizing them and achieving higher detection performance. It can be seen that FBCCA is expected to become a new standard paradigm after CCA.…”

Section: D) Filter Bank Canonical Correlation Analysis (Fbcca)mentioning

confidence: 99%

“…Dmochowski et al [102] proposed correlated components analysis (CORRCA) that calculates same spatial filters for two multichannel signals based on maximizing the linear components of the two. In 2018, Zhang et al [85] introduced the CORRCA to learn spatial filters with multiple trials of individual training data for SSVEP-based BCI systems, which is a potential technique to reduce background EEG activities. Zhang et al [84] further developed CORRCA to a two-stage architecture, that utilizes all the spatial filters obtained from all stimulus frequencies to improve the approach accuracy.…”

Section: D) Filter Bank Canonical Correlation Analysis (Fbcca)mentioning

confidence: 99%

Data Analytics in Steady-State Visual Evoked Potential-Based Brain–Computer Interface: A Review

et al. 2021

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Electroencephalograph (EEG) has been widely applied for brain-computer interface (BCI) which enables paralyzed people to directly communicate with and control of external devices, due to its portability, high temporal resolution, ease of use and low cost. Of various EEG paradigms, steady-state visual evoked potential (SSVEP)-based BCI system which uses multiple visual stimuli (such as LEDs or boxes on a computer screen) flickering at different frequencies has been widely explored in the past decades due to its fast communication rate and high signalto-noise ratio. In this paper, we review the current research in SSVEP-based BCI, focusing on the data analytics that enables continuous, accurate detection of SSVEPs and thus high information transfer rate. The main technical challenges, including signal pre-processing, spectrum analysis, signal decomposition, spatial filtering in particular canonical correlation analysis and its variations, and classification techniques are described in this paper. Research challenges and opportunities in spontaneous brain activities, mental fatigue, transfer learning as well as hybrid BCI are also discussed.

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Correlated Component Analysis for Enhancing the Performance of SSVEP-Based Brain-Computer Interface

Cited by 88 publications

References 46 publications

Group task-related component analysis (gTRCA): a multivariate method for inter-trial reproducibility and inter-subject similarity maximization for EEG data analysis

Group task-related component analysis (gTRCA): a multivariate method for inter-trial reproducibility and inter-subject similarity maximization for EEG data analysis

Two-Stage Frequency Recognition Method Based on Correlated Component Analysis for SSVEP-Based BCI

Data Analytics in Steady-State Visual Evoked Potential-Based Brain–Computer Interface: A Review

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