An MVMD-CCA Recognition Algorithm in SSVEP-Based BCI and Its Application in Robot Control

Wang, Kang; Zhai, Di‐Hua; Xiong, Yuhan; Hu, Leyun; Xia, Yuanqing

doi:10.1109/tnnls.2021.3135696

Cited by 36 publications

(8 citation statements)

References 34 publications

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“…A considerable improvement is obtained in terms of signal-to-noise ratio. An event-related potentials are detected based on spatio-temporal equalization and performance of ERP signals are enhanced based on EEG multichannel information [20]- [22], [24]. Multivariate autoregressive (MVAR) model is introduced to evaluate spatio-temporal correlation [24].…”

Section: Introductionmentioning

confidence: 99%

Adaptive filters based efficient EEG classification for steady state visually evoked potential based BCI system

Krishnappa¹,

Anandaraju²

2023

IJRES

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Brain-computer interfaces (BCIs) system is a link to generate a communication between disable people and physical devices. Thus, steady state visually evoked potential (SSVEP) is analysed to improve performance efficiency of BCIs system using multi-class classification process. Thus, an adaptive filtering-based component analysis (AFCA) method is adopted to examine SSVEP from multiple-channel electroencephalography (EEG) signals for BCIs system efficiency enhancement. Further, flickering at varied frequencies is used in a visual stimulation process to examine user intentions and brain responses. A detailed solution for optimization problem and efficient feature extraction is also presented. Here, a large SSVEP dataset is utilized which contains 256 channel EEG data. Experimental results are evaluated in terms of classification accuracy and information transfer rate to measure efficiency of proposed SSVEP extraction method against varied traditional SSVEP-based BCIs. The average information transfer rate (ITR) results are 308.23 bits per minute and classification accuracy is 93.48% using proposed AFCA method. Thus, proposed AFCA method shows decent performance in comparison with state-of-art-SSVEP extraction methods.

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Section: Introductionmentioning

confidence: 99%

Adaptive filters based efficient EEG classification for steady state visually evoked potential based BCI system

Krishnappa¹,

Anandaraju²

2023

IJRES

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“…To further boost the SSVEP detection performance, expansions of CCA developed from two aspects. On the one hand, CCA was combined with signal processing techniques, such as filter bank CCA (FBCCA) [11], binary subband CCA (BsCCA) [12], and multivariate variational mode decomposition CCA (MVMD-CCA) [13], to extract SSVEP harmonic frequency information for target recognition. On the other hand, individual calibration data was incorporated into CCA, such as L1-regularized multiway CCA (L1-MCCA) [14], multi-set CCA (MsetCCA) [15], individual template CCA (IT-CCA) [16], and extended CCA (eCCA) [17], to improve the SSVEP frequency detection performance.…”

Section: Introductionmentioning

confidence: 99%

Incorporating Neighboring Stimuli Data for Enhanced SSVEP-Based BCIs

Huang

Yang

Xiong

et al. 2022

IEEE Trans. Instrum. Meas.

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Various spatial filters have been proposed to enhance the target identification performance of steady-state visual evoked potential (SSVEP)-based brain-computer interfaces (BCIs). The current methods only extract the target-related information from the corresponding stimulus to learn the spatial filter parameter. However, the SSVEP data from neighboring stimuli also contain frequency information of the target stimulus, which could be utilized to further improve the target identification performance. In this paper, we propose a new method incorporating SSVEPs from the neighboring stimuli to strengthen the target-related frequency information. First, The spatial filter is obtained by maximizing the summation of covariances of SSVEP data corresponding to the target and its neighboring stimuli. Then the correlation features between spatially filtered templates and test data are calculated for target detection. For the performance evaluation, we implemented the offline experiment using the 40class benchmark dataset from 35 subjects and the 12-target selfcollected dataset from 11 subjects. Compared with the stateof-art spatial filtering methods, the proposed method showed superiority in classification accuracy and information transfer rate (ITR). The comparison results demonstrate the effectiveness of the proposed spatial filter for target identification in SSVEPbased BCIs.

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“…Among various brain signals, the steady-state visual evoked potential (SSVEP)-based BCI system has been widely explored because it is non-invasive, low cost, and has relatively high information transfer rates (ITR) and signal-to-noise ratio (SNR). In recent decades, SSVEP-based BCI technology has been applied in many applications, such as robotic manipulator grasping [2], speller system [3] and wheelchair control [4].…”

Section: Introductionmentioning

confidence: 99%