Unsupervised frequency-recognition method of SSVEPs using a filter bank implementation of binary subband CCA

Islam, Rabiul; Molla, Khademul Islam; Nakanishi, Masaki; Tanaka, Toshihisa

doi:10.1088/1741-2552/aa5847

Cited by 31 publications

(13 citation statements)

References 41 publications

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“…The accuracy of the CCA of the 4 s TW was 88.58%. Islam et al [43] reported that under the CCA-based method, the average accuracy of 10 subjects with 4 s TW for 12 stimuli reached about 93%, which was higher than the accuracy of this study. Chen et al [44] reported that under the four stimuli, the CCA-based method had reached 62% accuracy with a 4 s TW for nine subjects.…”

Section: Discussioncontrasting

confidence: 83%

Improvement of the Classification Accuracy of Steady-State Visual Evoked Potential-Based Brain-Computer Interfaces by Combining L1-MCCA with SVM

Gao

et al. 2021

Applied Sciences

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Canonical correlation analysis (CCA) has been used for the steady-state visual evoked potential (SSVEP) based brain-computer interface (BCI) for a long time. However, the reference signal of CCA is relatively simple and lacks subject-specific information. Moreover, over-fitting may occur when a short time window (TW) length was used in CCA. In this article, an optimized L1-regularized multiway canonical correlation analysis (L1-MCCA) is combined with a support vector machine (SVM) to overcome the aforementioned shortcomings in CCA. The correlation coefficients obtained by L1-MCCA were transferred into a particle-swarm-optimization (PSO)-optimized support vector machine (SVM) classifier to improve the classification accuracy. The performance of the proposed method was evaluated and compared with the traditional CCA and power spectral density (PSD) methods. The results showed that the accuracy of the L1-MCCA-PSO-SVM was 96.36% and 98.18% respectively when the TW lengths were 2 s and 6 s. This accuracy is higher than that of the traditional CCA and PSD methods.

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Section: Discussioncontrasting

confidence: 83%

Improvement of the Classification Accuracy of Steady-State Visual Evoked Potential-Based Brain-Computer Interfaces by Combining L1-MCCA with SVM

Gao

et al. 2021

Applied Sciences

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“…The CCA algorithm is improved based on the characteristics of the EEG evoked by this paradigm, which is used in training scenarios. The algorithm uses the BsCCA algorithm to improve the recognition accuracy of high-frequency stimuli [19] and uses the dynamic window method to improve the ITR further.…”

Section: Methodsmentioning

confidence: 99%

“…During the experiment, the SNR of the EEG signal in the high-frequency part was lower than that in the low-frequency part, which decreased the recognition accuracy of high-frequency components of some subjects. The BsCCA algorithm proposed by Islam et al can improve the recognition accuracy of high-frequency stimuli [19]. As shown in Fig.…”

Section: Methodsmentioning

confidence: 99%

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An extended binary subband canonical correlation analysis detection algorithm oriented to the radial contraction-expansion motion steady-state visual evoked paradigm

Zhao

Wang

et al. 2022

Brain Science Advances

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The radial contraction-expansion motion paradigm is a novel steady-state visual evoked experimental paradigm, and the electroencephalography (EEG) evoked potential is different from the traditional luminance modulation paradigm. The signal energy is concentrated chiefly in the fundamental frequency, while the higher harmonic power is lower. Therefore, the conventional steady-state visual evoked potential recognition algorithms optimizing multiple harmonic response components, such as the extended canonical correlation analysis (eCCA) and task-related component analysis (TRCA) algorithm, have poor recognition performance under the radial contraction-expansion motion paradigm. This paper proposes an extended binary subband canonical correlation analysis (eBSCCA) algorithm for the radial contraction-expansion motion paradigm. For the radial contraction-expansion motion paradigm, binary subband filtering was used to optimize the weighting coefficients of different frequency response signals, thereby improving the recognition performance of EEG signals. The results of offline experiments involving 13 subjects showed that the eBSCCA algorithm exhibits a better performance than the eCCA and TRCA algorithms under the stimulation of the radial contraction-expansion motion paradigm. In the online experiment, the average recognition accuracy of 13 subjects was 88.68% ± 6.33%, and the average information transmission rate (ITR) was 158.77 ± 43.67 bits/min, which proved that the algorithm had good recognition effect signals evoked by the radial contraction-expansion motion paradigm.

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“…The frequency that gives the maximum canonical correlation is recognized as the frequency of the target that the user is gazing at. This approach can be improved by applying a bank of bandpass filters [157,158]. Recent studies have pointed out that the nonuniform spectra of the spontaneous or background EEG can deteriorate the performances of the frequency recognition algorithm and have proposed efficient methods to achieve frequency recognition based on the effects of background EEG [159][160][161].…”

Section: Evoked Potentialsmentioning

confidence: 99%

An Introductory Tutorial on Brain–Computer Interfaces and Their Applications

et al. 2021

Self Cite

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The prospect and potentiality of interfacing minds with machines has long captured human imagination. Recent advances in biomedical engineering, computer science, and neuroscience are making brain–computer interfaces a reality, paving the way to restoring and potentially augmenting human physical and mental capabilities. Applications of brain–computer interfaces are being explored in applications as diverse as security, lie detection, alertness monitoring, gaming, education, art, and human cognition augmentation. The present tutorial aims to survey the principal features and challenges of brain–computer interfaces (such as reliable acquisition of brain signals, filtering and processing of the acquired brainwaves, ethical and legal issues related to brain–computer interface (BCI), data privacy, and performance assessment) with special emphasis to biomedical engineering and automation engineering applications. The content of this paper is aimed at students, researchers, and practitioners to glimpse the multifaceted world of brain–computer interfacing.

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Unsupervised frequency-recognition method of SSVEPs using a filter bank implementation of binary subband CCA

Abstract: 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.

Cited by 31 publications

References 41 publications

Improvement of the Classification Accuracy of Steady-State Visual Evoked Potential-Based Brain-Computer Interfaces by Combining L1-MCCA with SVM

Improvement of the Classification Accuracy of Steady-State Visual Evoked Potential-Based Brain-Computer Interfaces by Combining L1-MCCA with SVM

An extended binary subband canonical correlation analysis detection algorithm oriented to the radial contraction-expansion motion steady-state visual evoked paradigm

An Introductory Tutorial on Brain–Computer Interfaces and Their Applications

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