Assessment of high-frequency steady-state visual evoked potentials from below-the-hairline areas for a brain-computer interface based on Depth-of-Field

Floriano, Alan; Delisle-Rodríguez, Denis; Díez, Pablo; Bastos, Teodiano

doi:10.1016/j.cmpb.2019.105271

Cited by 9 publications

(2 citation statements)

References 33 publications

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“…Therefore, a better stimulation paradigm needed to be developed to achieve high ITR in the hairless area, such as by introducing spatial coding to SSVEP-BCI. Floriano et al designed a high-frequency SSVEP-BCI system behind the ear based on depth-of-field vision (Floriano et al 2020). Carmona et al studied the characteristics of the EEG response behind the ear under simultaneous multisensory stimulation (Carmona et al 2020).…”

Section: Background and Purposementioning

confidence: 99%

Optimizing a left and right visual field biphasic stimulation paradigm for SSVEP-based BCIs with hairless region behind the ear

Liang¹,

Bin²,

Chen³

et al. 2021

J. Neural Eng.

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Objective. Steady-state visual evoked potential (SSVEP) based brain-computer interface (BCI) has the characteristics of fast communication speed, high stability, and wide applicability, thus it has been widely studied. With the rapid development in paradigm, algorithm, and system design, SSVEP-BCI is gradually applied in clinical and real-life scenarios. In order to improve the ease of use of the SSVEP-BCI system, many studies have been focusing on developing it on the hairless area, but due to the lack of redesigning the stimulation paradigm to better adapt to the new area, the EEG response in the hairless area is worse than occipital region. Approach. This study first proposed a phase difference estimation method based on stimulating the left and right visual field separately, then developed and optimized a left and right visual field biphasic stimulation paradigm for SSVEP-based BCIs with hairless region behind the ear. Main results. In the 12-target online experiment, after a short model estimation training, all sixteen subjects used their best stimulus condition. The paradigm designed in this study can increase the proportion of applicable subjects for the behind-ear SSVEP-BCI system from 58.3% to 75% and increase the accuracy from 74.6±20.0% (the existing best SSVEP stimulus with hairless region behind the ear) to 84.2±14.7%, and the ITR from 14.2±6.4bits/min to 17.8±5.7bits/min. Significance. These results demonstrated that the proposed paradigm can effectively improve the BCI performance using the signal from the hairless region behind the ear, compared with the standard SSVEP stimulation paradigm.

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Section: Background and Purposementioning

confidence: 99%

Optimizing a left and right visual field biphasic stimulation paradigm for SSVEP-based BCIs with hairless region behind the ear

Liang¹,

Bin²,

Chen³

et al. 2021

J. Neural Eng.

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“…(3) If the variable X does not pass the F-test test [38], indicating that the feature variable X is not significantly correlated with the target variable Y , then delete X .…”

mentioning

confidence: 99%

Stepwise Discriminant Analysis based Optimal Frequency Band Selection and Ensemble Learning for Same Limb MI Recognition

Meng,

Zhu,

et al. 2024

Preprint

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Same limb motor imagery (MI) brain-computer interfaces can effectively overcome the cognitive disassociation problem of the traditional different-limb MI paradigm, and they can reduce the patient burden and extend the functionality of external devices more effectively. However, the electroencephalogram (EEG) MI features of same limb originate from one side of the brain, which poses a great challenge to MI EEG feature mining and selection as well as accurate decoding. To overcome this problem, we propose an adaptive feature selection strategy for subject-specific optimal frequency band based on regularized common spatial pattern (RCSP) and stepwise discriminant analysis, then combine the integrated classification strategy to accurately decode three types of single-limb MI tasks. As there are minor frequency band differences and huge variability for the same limb MI tasks, the optimal frequency band range for each subject was selected by stepwise discriminant analysis, and RCSP was used to extract spatial distribution features, which reduced the influence of the length of the time window and differences of the frequency bands. Then an integrated classification strategy based on multiple efficient classifiers is used for MI accurate recognition. The proposed method obtains 76.58% accuracy in the unilateral limb MI recognition task, which is 12.67%, 9.89%, 6.62%, and 7.90% higher than other traditional decoding methods such as CSP + LDA, FBCSP + LDA, FBCSP + C2CM, and FBCSP + SVM, respectively. Compared with Deep ConvNet and EEGNet, the decoding accuracy is improved by 16.93% and 7.33%, respectively. The experimental results show that our proposed highly efficient method improves the decoding accuracy for classifying different joints of unilateral limbs and has high promotion and application value.

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Biopotential Acquisition Systems

Guerrero

Spinelli

2021

Medicine-Based Informatics and Engineering

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Assessment of high-frequency steady-state visual evoked potentials from below-the-hairline areas for a brain-computer interface based on Depth-of-Field

Cited by 9 publications

References 33 publications

Optimizing a left and right visual field biphasic stimulation paradigm for SSVEP-based BCIs with hairless region behind the ear

Optimizing a left and right visual field biphasic stimulation paradigm for SSVEP-based BCIs with hairless region behind the ear

Stepwise Discriminant Analysis based Optimal Frequency Band Selection and Ensemble Learning for Same Limb MI Recognition

Biopotential Acquisition Systems

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