Journal ArticleDOI
Unsupervised frequency-recognition method of SSVEPs using a filter bank implementation of binary subband CCA.
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TLDR
The results suggest that BsCCA significantly improves the performance of SSVEP-based BCI compared to the state-of-the-art methods and can be usable in real world applications.Abstract:
Objective Recently developed effective methods for detection commands of steady-state visual evoked potential (SSVEP)-based brain-computer interface (BCI) that need calibration for visual stimuli, which cause more time and fatigue prior to the use, as the number of commands increases. This paper develops a novel unsupervised method based on canonical correlation analysis (CCA) for accurate detection of stimulus frequency. Approach A novel unsupervised technique termed as binary subband CCA (BsCCA) is implemented in a multiband approach to enhance the frequency recognition performance of SSVEP. In BsCCA, two subbands are used and a CCA-based correlation coefficient is computed for the individual subbands. In addition, a reduced set of artificial reference signals is used to calculate CCA for the second subband. The analyzing SSVEP is decomposed into multiple subband and the BsCCA is implemented for each one. Then, the overall recognition score is determined by a weighted sum of the canonical correlation coefficients obtained from each band. Main results A 12-class SSVEP dataset (frequency range: 9.25-14.75 Hz with an interval of 0.5 Hz) for ten healthy subjects are used to evaluate the performance of the proposed method. The results suggest that BsCCA significantly improves the performance of SSVEP-based BCI compared to the state-of-the-art methods. The proposed method is an unsupervised approach with averaged information transfer rate (ITR) of 77.04 bits min-1 across 10 subjects. The maximum individual ITR is 107.55 bits min-1 for 12-class SSVEP dataset, whereas, the ITR of 69.29 and 69.44 bits min-1 are achieved with CCA and NCCA respectively. Significance The statistical test shows that the proposed unsupervised method significantly improves the performance of the SSVEP-based BCI. It can be usable in real world applications.read more
Citations
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Journal ArticleDOI
To train or not to train? A survey on training of feature extraction methods for SSVEP-based BCIs.
TL;DR: This paper reviews the various state-of-the-art SSVEP feature extraction methods that have been developed and are most widely used in the literature and highlights the strengths and weaknesses of the three categories of SSVEp training methods.
Journal ArticleDOI
Correlated Component Analysis for Enhancing the Performance of SSVEP-Based Brain-Computer Interface
Yangsong Zhang,Daqing Guo,Fali Li,Erwei Yin,Yu Zhang,Peiyang Li,Qibin Zhao,Toshihisa Tanaka,Dezhong Yao,Peng Xu +9 more
TL;DR: This work proposes a CORCA algorithm to learn spatial filters with multiple blocks of individual training data for SSVEP-based BCI scenario, and shows that the proposed method significantly outperforms the TRCA-based method.
Journal ArticleDOI
Two-Stage Frequency Recognition Method Based on Correlated Component Analysis for SSVEP-Based BCI
Yangsong Zhang,Erwei Yin,Fali Li,Yu Zhang,Toshihisa Tanaka,Qibin Zhao,Yan Cui,Peng Xu,Dezhong Yao,Daqing Guo +9 more
TL;DR: Wang et al. as discussed by the authors proposed using correlated component analysis (CORRCA) rather than canonical correlation analysis (CCA) for frequency recognition in steady-state visual evoked potential (SSVEP)-based brain-computer interface (BCI) systems.
Journal ArticleDOI
A novel training-free recognition method for SSVEP-based BCIs using dynamic window strategy.
TL;DR: This study proposed a new training-free dynamical optimization algorithm, which significantly improved the performance of online SSVEP-based BCI systems and significantly outperforms STE-DW and FBCCA-FW in terms of accuracy and ITR.
Journal ArticleDOI
Multiband tangent space mapping and feature selection for classification of EEG during motor imagery.
TL;DR: The increased classification accuracy of MI tasks with the proposed MTSMS approach can yield effective implementation of BCI.
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