Latent common source extraction via a generalized canonical correlation framework for frequency recognition in SSVEP based brain–computer interfaces

Kumar, G R Kiran; Reddy, M. Ramasubba

doi:10.1088/1741-2552/ab13d1

Cited by 3 publications

(2 citation statements)

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“…The LCSE algorithm (Kumar and Reddy, 2019a) extracted a latent common source feature space from the multiple EEG trials, and the researchers called such process as “group configuration”. By such feature space expression, the recognition performance of SSVEP can be improved by the LCSE algorithm.…”

Section: Ssvep Recognition Algorithm Based On Template Matching Of Tr...mentioning

confidence: 99%

“…In addition, the multiple-set CCA (MsetCCA) algorithm (Jiao et al , 2018) learned the optimal matching template from multiple sets of existed training trials by a joint spatial filter, and then computed the correlation coefficients for the recognition of the following EEG trials. In recent years, a series of component analysis algorithms based on the SSVEP feature space projections have been proposed, including task-related component analysis (TRCA) algorithm (Nakanishi et al , 2017), latent common source extraction (LCSE) algorithm (Kumar and Reddy, 2019a) and sum of squared correlations (SSCOR) algorithm (Kumar and Reddy, 2019b). These algorithms all solved the optimal projection matrix from their respective perspectives, projected the existed EEG training trials to the new feature space that contained the SSVEP components, and then performed the optimal template matching for SSVEP categories recognition using the projected feature space.…”

Section: Introductionmentioning

confidence: 99%

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A comparative survey of SSVEP recognition algorithms based on template matching of training trials

Luo

2022

IJICC

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PurposeSteady-state visual evoked potential (SSVEP) has been widely used in the application of electroencephalogram (EEG) based non-invasive brain computer interface (BCI) due to its characteristics of high accuracy and information transfer rate (ITR). To recognize the SSVEP components in collected EEG trials, a lot of recognition algorithms based on template matching of training trials have been proposed and applied in recent years. In this paper, a comparative survey of SSVEP recognition algorithms based on template matching of training trails has been done.Design/methodology/approachTo survey and compare the recently proposed recognition algorithms for SSVEP, this paper regarded the conventional canonical correlated analysis (CCA) as the baseline, and selected individual template CCA (ITCCA), multi-set CCA (MsetCCA), task related component analysis (TRCA), latent common source extraction (LCSE) and a sum of squared correlation (SSCOR) for comparison.FindingsFor the horizontal comparative of the six surveyed recognition algorithms, this paper adopted the “Tsinghua JFPM-SSVEP” data set and compared the average recognition performance on such data set. The comparative contents including: recognition accuracy, ITR, correlated coefficient and R-square values under different time duration of the SSVEP stimulus presentation. Based on the optimal time duration of stimulus presentation, the author has also compared the efficiency of the six compared algorithms. To measure the influence of different parameters, the number of training trials, the number of electrodes and the usage of filter bank preprocessing were compared in the ablation study.Originality/valueBased on the comparative results, this paper analyzed the advantages and disadvantages of the six compared SSVEP recognition algorithms by considering application scenes, real-time and computational complexity. Finally, the author gives the algorithms selection range for the recognition of real-world online SSVEP-BCI.

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Section: Ssvep Recognition Algorithm Based On Template Matching Of Tr...mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A comparative survey of SSVEP recognition algorithms based on template matching of training trials

Luo

2022

IJICC

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Signal processing algorithms for SSVEP-based brain computer interface: State-of-the-art and recent developments

Hong

Qin

2021

IFS

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Over past two decades, steady-state evoked potentials (SSVEP)-based brain computer interface (BCI) systems have been extensively developed. As we all know, signal processing algorithms play an important role in this BCI. However, there is no comprehensive review of the latest development of signal processing algorithms for SSVEP-based BCI. By analyzing the papers published in authoritative journals in nearly five years, signal processing algorithms of preprocessing, feature extraction and classification modules are discussed in detail. In addition, other aspects existed in this BCI are mentioned. The following key problems are solved. (1) In recent years, which signal processing algorithms are frequently used in each module? (2) Which signal processing algorithms attract more attention in recent years? (3) Which modules are the key to signal processing in BCI field? This information is very important for choosing the appropriate algorithms, and can also be considered as a reference for further research. Simultaneously, we hope that this work can provide relevant BCI researchers with valuable information about the latest trends of signal processing algorithms for SSVEP-based BCI systems.

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Enhancing SSVEP-Based Brain-Computer Interface with Two-Step Task-Related Component Analysis

Lee

Choi

2021

Sensors

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Among various methods for frequency recognition of the steady-state visual evoked potential (SSVEP)-based brain-computer interface (BCI) study, a task-related component analysis (TRCA), which extracts discriminative spatial filters for classifying electroencephalogram (EEG) signals, has gathered much interest. The TRCA-based SSVEP method yields lower computational cost and higher classification performance compared to existing SSVEP methods. In spite of its utility, the TRCA-based SSVEP method still suffers from the degradation of the frequency recognition rate in cases where EEG signals with a short length window are used. To address this issue, here, we propose an improved strategy for decoding SSVEPs, which is insensitive to a window length by carrying out two-step TRCA. The proposed method reuses the spatial filters corresponding to target frequencies generated by the TRCA. Followingly, the proposed method accentuates features for target frequencies by correlating individual template and test data. For the evaluation of the performance of the proposed method, we used a benchmark dataset with 35 subjects and confirmed significantly improved performance comparing with other existing SSVEP methods. These results imply the suitability as an efficient frequency recognition strategy for SSVEP-based BCI applications.

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Latent common source extraction via a generalized canonical correlation framework for frequency recognition in SSVEP based brain–computer interfaces

Cited by 3 publications

References 29 publications

A comparative survey of SSVEP recognition algorithms based on template matching of training trials

A comparative survey of SSVEP recognition algorithms based on template matching of training trials

Signal processing algorithms for SSVEP-based brain computer interface: State-of-the-art and recent developments

Enhancing SSVEP-Based Brain-Computer Interface with Two-Step Task-Related Component Analysis

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