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

Ming, Gege; Chen, Hongda; Gao, Xiaorong; Wang, Yijun

doi:10.1088/1741-2552/ac284a

Cited by 25 publications

(58 citation statements)

References 33 publications

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“…The hybrid accuracy can be improved by customizing the weighted coefficient of the hybrid method for each participant due to the clear individual differences in the system performance. Secondly, the optimization of the grid stimulus paradigm may be helpful for increasing ITR and reducing the flickering sensation of visual stimulation, including the spatial frequency, the proportion of stimulation area, and illuminance (Ming et al, 2021). More entrained VEPs can be generated and enhanced by stimuli containing spatial contrast than the spatially uniform stimuli (Williams et al, 2004), and the spatial frequency can be optimized to maximize the ITR and reduce the visual irritation (Waytowich et al, 2017).…”

Section: Discussionmentioning

confidence: 99%

“…The size of each stimulus was 75 × 75 pixels (3 • × 3 • ), and the horizontal and vertical distances between the centers of any two adjacent stimuli were 225 pixels (9 • ). Each target adopted the style of a grid stimulus (Ming et al, 2021), which consisted of 8 × 8 small flickering squares. The size of each square was 5 × 5 pixels.…”

Section: Visual Stimulus Designmentioning

confidence: 99%

“…By applying a binary linear classifier to PR and SSVEP signals for 18 subjects, hybrid classification accuracy was 83% with the data length of 7.5 s, which was higher than that of PR (75%) and SSVEP (80%). These PR-based HCI and h-BCI studies showed limitations, such as the small number of targets and the longer detection time compared with the existing SSVEP-BCIs (Nakanishi et al, 2018;Mao et al, 2020;Ming et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

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A Hybrid Brain-Computer Interface Based on Visual Evoked Potential and Pupillary Response

Jiang

Gao

et al. 2022

Front. Hum. Neurosci.

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Brain-computer interface (BCI) based on steady-state visual evoked potential (SSVEP) has been widely studied due to the high information transfer rate (ITR), little user training, and wide subject applicability. However, there are also disadvantages such as visual discomfort and “BCI illiteracy.” To address these problems, this study proposes to use low-frequency stimulations (12 classes, 0.8–2.12 Hz with an interval of 0.12 Hz), which can simultaneously elicit visual evoked potential (VEP) and pupillary response (PR) to construct a hybrid BCI (h-BCI) system. Classification accuracy was calculated using supervised and unsupervised methods, respectively, and the hybrid accuracy was obtained using a decision fusion method to combine the information of VEP and PR. Online experimental results from 10 subjects showed that the averaged accuracy was 94.90 ± 2.34% (data length 1.5 s) for the supervised method and 91.88 ± 3.68% (data length 4 s) for the unsupervised method, which correspond to the ITR of 64.35 ± 3.07 bits/min (bpm) and 33.19 ± 2.38 bpm, respectively. Notably, the hybrid method achieved higher accuracy and ITR than that of VEP and PR for most subjects, especially for the short data length. Together with the subjects’ feedback on user experience, these results indicate that the proposed h-BCI with the low-frequency stimulation paradigm is more comfortable and favorable than the traditional SSVEP-BCI paradigm using the alpha frequency range.

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Section: Discussionmentioning

confidence: 99%

Section: Visual Stimulus Designmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Hybrid Brain-Computer Interface Based on Visual Evoked Potential and Pupillary Response

Jiang

Gao

et al. 2022

Front. Hum. Neurosci.

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“…SSVEP-BCIs also require a monitor and visual stimuli. The stimuli of SSVEP can make the user's eyes tired [13].…”

Section: Introductionmentioning

confidence: 99%

Studies to Overcome Brain–Computer Interface Challenges

Choi

Yeom

2022

Applied Sciences

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A brain–computer interface (BCI) is a promising technology that can analyze brain signals and control a robot or computer according to a user’s intention. This paper introduces our studies to overcome the challenges of using BCIs in daily life. There are several methods to implement BCIs, such as sensorimotor rhythms (SMR), P300, and steady-state visually evoked potential (SSVEP). These methods have different pros and cons according to the BCI type. However, all these methods are limited in choice. Controlling the robot arm according to the intention enables BCI users can do various things. We introduced the study predicting three-dimensional arm movement using a non-invasive method. Moreover, the study was described compensating the prediction using an external camera for high accuracy. For daily use, BCI users should be able to turn on or off the BCI system because of the prediction error. The users should also be able to change the BCI mode to the efficient BCI type. The BCI mode can be transformed based on the user state. Our study was explained estimating a user state based on a brain’s functional connectivity and a convolutional neural network (CNN). Additionally, BCI users should be able to do various tasks, such as carrying an object, walking, or talking simultaneously. A multi-function BCI study was described to predict multiple intentions simultaneously through a single classification model. Finally, we suggest our view for the future direction of BCI study. Although there are still many limitations when using BCI in daily life, we hope that our studies will be a foundation for developing a practical BCI system.

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“…BCI systems based on low-frequency SSVEP can cause severe visual fatigue in users because the visual stimulation is too intense for them [10,11]. Mediumfrequency SSVEP has a certain chance of causing seizures in users.…”

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confidence: 99%

A method for identifying high-frequency steady-state visual evoked potential signals based on ensemble empirical mode decomposition

Hua

Chen

et al. 2022

Preprint

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In brain-computer interface (BCI) systems, the accurate identification of steady-state visual evoked potentials (SSVEP) is crucial in fields such as biomedicine, and various artifacts affect the identification accuracy making the signals difficult to identify. To identify steady-state visual evoked potentials, analysis is generally performed in the frequency domain to characterize the periodicity of the signal, more specifically, spectral analysis is required to characterize the distribution of electroencephalogram (EEG) signal power along the frequency. The high-frequency SSVEP has advantages such as less ocular stimulation for the subject but is difficult to identify. A method based on ensemble empirical modal decomposition (EEMD) is proposed to address the difficulty of identifying high-frequency steady-state visual evoked potentials. Decompose the three-channel data of Oz, O1 and O2 obtained from the experiment, perform spectrum analysis on the intrinsic mode function obtained by the decomposition, and select some effective intrinsic mode functions according to the obtained spectrogram and recombine them into new signals to replace the original signal. The multivariate simultaneous exponential (MSI) algorithm combined with the ensemble empirical modal decomposition was used to classify the reconstituted obtained signals, and the EEMD-MSI algorithm obtained better results compared with the direct classification method using the MSI algorithm.The average accuracy of the EEMD-MSI algorithm was improved by 9.957% and 16.424% compared with the MSI algorithm under the 3s and 4s time windows, respectively, and the highest accuracy reached 74.97%, 92.12%, respectively.

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

Cited by 25 publications

References 33 publications

A Hybrid Brain-Computer Interface Based on Visual Evoked Potential and Pupillary Response

A Hybrid Brain-Computer Interface Based on Visual Evoked Potential and Pupillary Response

Studies to Overcome Brain–Computer Interface Challenges

A method for identifying high-frequency steady-state visual evoked potential signals based on ensemble empirical mode decomposition

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