Robust frequency recognition for SSVEP-based BCI with temporally local multivariate synchronization index

Zhang, Yangsong; Guo, Daqing; Xu, Peng; Zhang, Yu; Yao, Dezhong

doi:10.1007/s11571-016-9398-9

Cited by 52 publications

(40 citation statements)

References 40 publications

(38 reference statements)

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“…Using lately introduced methods (e.g. multivariate synchronization index [ 35 ], temporally local multivariate synchronization index [ 36 ]) to detect the frequency in SSVEPs can be a subject of future study.…”

Section: Discussionmentioning

confidence: 99%

Steady state visual evoked potential (SSVEP) based brain-computer interface (BCI) performance under different perturbations

İşcan

Nikulin

2018

PLoS ONE

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Brain-computer interface (BCI) paradigms are usually tested when environmental and biological artifacts are intentionally avoided. In this study, we deliberately introduced different perturbations in order to test the robustness of a steady state visual evoked potential (SSVEP) based BCI. Specifically we investigated to what extent a drop in performance is related to the degraded quality of EEG signals or rather due to increased cognitive load. In the online tasks, subjects focused on one of the four circles and gave feedback on the correctness of the classification under four conditions randomized across subjects: Control (no perturbation), Speaking (counting loudly and repeatedly from one to ten), Thinking (mentally counting repeatedly from one to ten), and Listening (listening to verbal counting from one to ten). Decision tree, Naïve Bayes and K-Nearest Neighbor classifiers were used to evaluate the classification performance using features generated by canonical correlation analysis. During the online condition, Speaking and Thinking decreased moderately the mean classification accuracy compared to Control condition whereas there was no significant difference between Listening and Control conditions across subjects. The performances were sensitive to the classification method and to the perturbation conditions. We have not observed significant artifacts in EEG during perturbations in the frequency range of interest except in theta band. Therefore we concluded that the drop in the performance is likely to have a cognitive origin. During the Listening condition relative alpha power in a broad area including central and temporal regions primarily over the left hemisphere correlated negatively with the performance thus most likely indicating active suppression of the distracting presentation of the playback. This is the first study that systematically evaluates the effects of natural artifacts (i.e. mental, verbal and audio perturbations) on SSVEP-based BCIs. The results can be used to improve individual classification performance taking into account effects of perturbations.

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Section: Discussionmentioning

confidence: 99%

Steady state visual evoked potential (SSVEP) based brain-computer interface (BCI) performance under different perturbations

İşcan

Nikulin

2018

PLoS ONE

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“…For SSVEP detection or recognition in BCI applications, many researchers have con rmed that a sophisticated calibration with appropriate analysis method could signi cantly improve the accuracy [25]. The extended CCA methods containing MCCA [22], L1-MCCA [23], MsetCCA [24] and CFA [25] Furthermore, other new techniques also have been proved to outperform the CCA method in SSVEP recognition, such as multivariate synchronization index (MSI) [17], temporally local MSI (TMSI) [4] and likelihood ratio test (LRT) [18]. However, the essence of these methods is the same as CCA, which is to investigate the relationships between two sets of variables.…”

Section: Tw (S)mentioning

confidence: 99%

“…In the past few years, a range of BCI systems have been investigated including P300 [2,3], steady state visual evoked potential (SSVEP) [4,5], motor imagery [6] and hybrid BCI system [7,8]. In particular, SSVEP-based BCIs have attracted widespread interest because of its high information transfer rate (ITR), high signal-to-noise ratio (SNR), and minimal training [9,10].…”

Section: Introductionmentioning

confidence: 99%

“…Multivariate synchronization index (MSI) [17] detects the SSVEP by measuring the synchronization between recorded EEG and pre-constructed reference signals with the same frequency of visual stimuli. The temporally local MSI (TMSI) [4] improves the original MSI using temporally local information of signals. And likelihood ratio test (LRT) [18] is another tool to measuring the correlation between two set of multivariate signals and has been used in frequency detection of SSVEP-based BCIs.…”

Section: Introductionmentioning

confidence: 99%

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A new multivariate empirical mode decomposition method for improving the performance of SSVEP-based brain–computer interface

et al. 2017

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“…For the noninvasive BCI, the scalp electroencephalogram (EEG) is the most-used modality to convey the user's intentions owing to its low cost and high portability for well-defined paradigms [ 5 ]. The well-designed paradigms in EEG-based BCIs include motor imagery [ 6 , 7 ], steady-state visual evoked potentials (SSVEPs) [ 8 – 10 ], P300 event-related potentials [ 11 , 12 ], and motion-onset visual evoked potential (mVEP) [ 13 , 14 ]. Among these, mVEP is an important measure for studying the motion vision processing mechanisms of humans and animals.…”

Section: Introductionmentioning

confidence: 99%

An Adaptive Calibration Framework for mVEP-Based Brain-Computer Interface

et al. 2018

Computational and Mathematical Methods in Medicine

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Electroencephalogram signals and the states of subjects are nonstationary. To track changing states effectively, an adaptive calibration framework is proposed for the brain-computer interface (BCI) with the motion-onset visual evoked potential (mVEP) as the control signal. The core of this framework is to update the training set adaptively for classifier training. The updating procedure consists of two operations, that is, adding new samples to the training set and removing old samples from the training set. In the proposed framework, a support vector machine (SVM) and fuzzy C-mean clustering (fCM) are combined to select the reliable samples for the training set from the blocks close to the current blocks to be classified. Because of the complementary information provided by SVM and fCM, they can guarantee the reliability of information fed into classifier training. The removing procedure will aim to remove those old samples recorded a relatively long time before current new blocks. These two operations could yield a new training set, which could be used to calibrate the classifier to track the changing state of the subjects. Experimental results demonstrate that the adaptive calibration framework is effective and efficient and it could improve the performance of online BCI systems.

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Robust frequency recognition for SSVEP-based BCI with temporally local multivariate synchronization index

Cited by 52 publications

References 40 publications

Steady state visual evoked potential (SSVEP) based brain-computer interface (BCI) performance under different perturbations

Steady state visual evoked potential (SSVEP) based brain-computer interface (BCI) performance under different perturbations

A new multivariate empirical mode decomposition method for improving the performance of SSVEP-based brain–computer interface

An Adaptive Calibration Framework for mVEP-Based Brain-Computer Interface

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