Improving user experience of SSVEP BCI through low amplitude depth and high frequency stimuli design

Ladouce, Simon; Darmet, Ludovic; Tresols, Juan Jesus Torre; Ferraro, Giuseppe; Dehais, Frédéric

doi:10.1038/s41598-022-12733-0

Cited by 33 publications

(41 citation statements)

References 40 publications

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“…The low-frequency (4-12 Hz) and mediumfrequency (12)(13)(14)(15)(16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30) SSVEPs are preferred by the BCI system due to advantages in high amplitude and signal-to-noise ratio (SNR) [2][3][4][5][6][7][8][9][10][11][12][13]. A 40-target SSVEP-based BCI speller with stimulation frequencies from 8 to 15.8 Hz had reported a high information transfer rate (ITR) of 325 bits min −1 , through which the user could input a character every 0.8 s [4].…”

Section: Introductionmentioning

confidence: 99%

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A new grid stimulus with subtle flicker perception for user-friendly SSVEP-based BCIs

et al. 2023

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Objective. The traditional uniform flickering stimulation pattern shows strong steady-state visual evoked potential (SSVEP) responses and poor user experience with intense flicker perception. To achieve a balance between performance and comfort in SSVEP-based brain-computer interface (BCI) systems, this study proposed a new grid stimulation pattern with reduced stimulation area and low spatial contrast. Approach. A spatial contrast scanning experiment was conducted first to clarify the relationship between the SSVEP characteristics and the signs and values of spatial contrast. Four stimulation patterns were involved in the experiment: the ON and OFF grid stimulation patterns that separately activated the positive or negative contrast information processing pathways, the ON-OFF grid stimulation pattern that simultaneously activated both pathways, and the uniform flickering stimulation pattern that served as a control group. The contrast-intensity and contrast-user experience curves were obtained for each stimulation pattern. Accordingly, the optimized stimulation schemes with low spatial contrast (the ON-50% grid stimulus, the OFF-50% grid stimulus, and the Flicker-30% stimulus) were applied in a 12-target and a 40-target BCI speller and compared with the traditional uniform flickering stimulus (the Flicker-500% stimulus) in the evaluation of BCI performance and subjective experience. Main results. The OFF-50% grid stimulus showed comparable online performance (12-target, 2 s: 69.87±0.74 vs. 69.76±0.58 bits min-1, 40-target, 4 s: 57.02±2.53 vs. 60.79±1.08 bits min-1) and improved user experience (better comfortable level, weaker flicker perception and higher preference level) compared to the traditional Flicker-500% stimulus in both multi-targets BCI spellers. Significance. Selective activation of the negative contrast information processing pathway using the new OFF-50% grid stimulus evoked robust SSVEP responses. On this basis, high-performance and user-friendly SSVEP-based BCIs have been developed and implemented, which has important theoretical significance and application value in promoting the development of the visual BCI technology.

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Section: Introductionmentioning

confidence: 99%

“…There are also numerous studies devoted to reducing the flickering sensation of low-frequency stimuli for different stimulation patterns [11,[14][15][16][17]. Properly increasing the duty cycle has been shown to be a solution for light stimuli to achieve reduced visual fatigue and acceptable SSVEP intensity [14].…”

Section: Introductionmentioning

confidence: 99%

A new grid stimulus with subtle flicker perception for user-friendly SSVEP-based BCIs

et al. 2023

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“…In contrast to low-and medium-frequency stimuli, the high-frequency stimulation is considered less annoying. Please note that high-frequency SSVEP is more suitable for building a comfortable system [30], [48], [49]. According to the comfort questionnaire results (see Fig.…”

Section: Discussionmentioning

confidence: 99%

“…6, the highest ITR of the first condition is obtained when the data length is 0.3 s. However, the corresponding classification accuracy is only 79.88 ± 3.66 %. A previous study shows that the reliable communication may be difficult when the classification accuracy is less than 80% [48]. When the data length is 0.6 s, an average accuracy rate of 86.41 ± 3.60 % is obtained.…”

Section: B Target Identification Performancementioning

confidence: 94%

Optimizing Stimulus Frequency Ranges for Building a High-Rate High Frequency SSVEP-BCI

Chen

Liu

Wang

et al. 2023

IEEE Trans. Neural Syst. Rehabil. Eng.

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The brain-computer interfaces (BCIs) based on steady-state visual evoked potential (SSVEP) have been extensively explored due to their advantages in terms of high communication speed and smaller calibration time. The visual stimuli in the low-and medium-frequency ranges are adopted in most of the existing studies for eliciting SSVEPs. However, there is a need to further improve the comfort of these systems. The high-frequency visual stimuli have been used to build BCI systems and are generally considered to significantly improve the visual comfort, but their performance is relatively low. The distinguishability of 16-class SSVEPs encoded by the three frequency ranges, i.e., 31-34.75 Hz with an interval of 0.25 Hz, 31-38.5 Hz with an interval of 0.5 Hz, 31-46 Hz with an interval of 1 Hz, is explored in this study. We compare classification accuracy and information transfer rate (ITR) of the corresponding BCI system. According to the optimized frequency range, this study builds an online 16-target high frequency SSVEP-BCI and verifies the feasibility of the proposed system based on 21 healthy subjects. The BCI based on visual stimuli with the narrowest frequency range, i.e., 31-34.5 Hz, have the highest ITR. Therefore, the narrowest frequency range is adopted to build an online 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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Improving user experience of SSVEP BCI through low amplitude depth and high frequency stimuli design

Cited by 33 publications

References 40 publications

A new grid stimulus with subtle flicker perception for user-friendly SSVEP-based BCIs

A new grid stimulus with subtle flicker perception for user-friendly SSVEP-based BCIs

Optimizing Stimulus Frequency Ranges for Building a High-Rate High Frequency SSVEP-BCI

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

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