Optimizing Spatial Contrast of a New Checkerboard Stimulus for Eliciting Robust SSVEPs

Ming, Gege; Wang, Yijun; Chen, Hongda

doi:10.1109/ner.2019.8716972

Cited by 6 publications

(16 citation statements)

References 13 publications

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“…Brain-computer interface (BCI) systems based on steady-state visual evoked potentials (SSVEPs) have attracted extensive interest with advantages of noninvasiveness, little user training, and high information transfer rate (ITR) [1,2]. Periodic visual stimuli could elicit SSVEPs up to at least 90 Hz and the broad band can be classified into three frequency regions: low (4-12 Hz), medium (12)(13)(14)(15)(16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30), and high (>30 Hz) frequency bands [3,4]. Existing SSVEP-based BCI systems usually favor the low-or medium-frequency regions for larger amplitude responses and higher ITRs.…”

Section: Introductionmentioning

confidence: 99%

“…The finding that spatial contrasts presented by the checkerboard stimulation may modulate SSVEPs provides inspiration for the design of new stimulation patterns. A new checkerboard-like stimulation scheme with spatially alternated flickering squares presenting stimulation signals and stationary background areas providing distinct background contrasts was proposed in [28]. Unlike the traditional contrast-reversing checkerboard stimulus that produces SSVEP responses more prominent only at the even times of the stimulation frequency, the new checkerboard-like stimuli elicited robust SSVEPs at both the odd and the even times of the stimulation frequency, which is more conducive to target detection in BCI systems.…”

Section: Introductionmentioning

confidence: 99%

“…Besides, stimulation patterns with higher spatial frequencies show less visually irritation compared with those with lower spatial frequencies. Different from the traditional checkerboard stimulus, the new checkerboardlike stimulus cuts the stimulation area in half [28]. It remains unclear whether or how the spatial contrasts and spatial frequencies affect its performance and visual comfort.…”

Section: Introductionmentioning

confidence: 99%

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Optimizing spatial properties of a new checkerboard-like visual stimulus for user-friendly SSVEP-based BCIs

Ming¹,

Chen²,

Gao³

et al. 2021

J. Neural Eng.

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Objective. Low-frequency steady-state visual evoked potential (SSVEP)-based brain–computer interface (BCI) systems with high performance are prone to cause visual discomfort and fatigue. High-frequency SSVEP-based BCI systems can alleviate the discomfort, but always obtain lower performance. This study optimized the spatial properties of a proposed checkerboard-like visual stimulus toward high-performance and user-friendly SSVEP-based BCI systems. Approach. On the one hand, two checkerboard-like stimuli with distinct spatial contrasts (the black- and white-background) were designed to balance the tradeoff between BCI performance and user experience and compared with the traditional flickering stimulus. On the other hand, the impacts of the spatial frequency of the new checkerboard-like stimulus on the flicker perception and the intensity of the elicited SSVEP were clarified. The SSVEP-based BCI systems were implemented based on the checkerboard-like stimuli under low-frequency and high-frequency conditions. The user experience for each stimulation pattern was estimated by questionnaires for subjective evaluation. Main results. The comparison results indicate that the black-background checkerboard-like stimulus with an optimized spatial frequency achieved comparable performance and enhanced visual comfort compared with the flickering stimulus. Furthermore, the online nine-target BCI system using the black-background checkerboard-like stimuli achieved averaged information transfer rates of 124.0 ± 2.3 and 109.0 ± 20.4 bits min−1 with low-frequency and high-frequency stimulation respectively. Significance. The new checkerboard-like stimuli with optimized properties show superiority of system performance and user experience in implementing SSVEP-based BCI, which will promote its practical applications in communication and control.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Optimizing spatial properties of a new checkerboard-like visual stimulus for user-friendly SSVEP-based BCIs

Ming¹,

Chen²,

Gao³

et al. 2021

J. Neural Eng.

Self Cite

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“…BCIs had been widely developed to allow subjects to control devices or communicate with others by modulating their brain signals [8][9][10][11][12][13][14][15][16][17][18]. Generally, the BCIs use near-infrared spectroscopy, functional magnetic resonance imaging, magnetoencephalography, or electroencephalogram (EEG) to monitor a user's brain activities [8][9][10].…”

Section: Introductionmentioning

confidence: 99%

“…Thus, the complexity of the signal process can be effectively reduced and it is suitable to develop practical applications. However, for the practical applications, the EEG signals always contain noises and the performances of SSVEP-based BCIs are severely degraded [16,17]. Thus, developing robust SSVEP-based BCIs allows to increase the performance and the values of SSVEP-based BCIs.…”

Section: Introductionmentioning

confidence: 99%

Denoising Autoencoder-Based Feature Extraction to Robust SSVEP-Based BCIs

Chen

et al. 2021

Sensors

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For subjects with amyotrophic lateral sclerosis (ALS), the verbal and nonverbal communication is greatly impaired. Steady state visually evoked potential (SSVEP)-based brain computer interfaces (BCIs) is one of successful alternative augmentative communications to help subjects with ALS communicate with others or devices. For practical applications, the performance of SSVEP-based BCIs is severely reduced by the effects of noises. Therefore, developing robust SSVEP-based BCIs is very important to help subjects communicate with others or devices. In this study, a noise suppression-based feature extraction and deep neural network are proposed to develop a robust SSVEP-based BCI. To suppress the effects of noises, a denoising autoencoder is proposed to extract the denoising features. To obtain an acceptable recognition result for practical applications, the deep neural network is used to find the decision results of SSVEP-based BCIs. The experimental results showed that the proposed approaches can effectively suppress the effects of noises and the performance of SSVEP-based BCIs can be greatly improved. Besides, the deep neural network outperforms other approaches. Therefore, the proposed robust SSVEP-based BCI is very useful for practical applications.

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