Abstract
Steady-state visual evoked potential (SSVEP)-based brain-computer interfaces (BCIs) have shown promising results in various applications, such as controlling prosthetic devices and augmented reality systems. However, current data-driven frequency recognition methods used to build high-performance SSVEP-BCIs often encounter overfitting and poor generalization when training data is limited. To address this issue, in this paper, we propose two potential SSVEP data augmentation methods, namely filter band masking (FBM) and random phase erasing (RPE), based on the inherent features of SSVEPs. These methods can produce high-quality supplementary training data to improve the performance of SSVEP-BCIs without parameter learning, making them easy to implement. To evaluate the proposed methods, two large-scale publicly available datasets (Benchmark and BETA) were used, and the experimental results showed that the proposed methods significantly could enhance the classification performance of baseline classifiers with a limited amount of calibration data. Specifically, evaluated on two methods, FBM increased the average accuracies by 7.40%, 8.55%, and RPE increased the average accuracies by 5.85%, 6.10%, respectively, with as few as two 1-scecond calibration trials on the Benchmark dataset. These findings demonstrate the potential of these data augmentation methods in enhancing the practicality of SSVEP-BCI for real-life scenarios.
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References
Yuan, X., Sun, Q., Zhang, L., Wang, H.: Enhancing detection of SSVEP-based BCIs via a novel CCA-based method. Biomed. Signal Process. Control 74, 103482 (2022)
Zhang, Y., Xu, P., Liu, T., Hu, J., Zhang, R., Yao, D.: Multiple frequencies sequential coding for SSVEP-based brain-computer interface. PLoS ONE 7(3), e29519 (2012)
Chen, X., Chen, Z., Gao, S., Gao, X.: A high-ITR SSVEP-based BCI speller. Brain-Computer Interfaces 1(3–4), 181–191 (2014)
Chen, X., Wang, Y., Nakanishi, M., Gao, X., Jung, T.P., Gao, S.: High-speed spelling with a noninvasive brain-computer interface. Proc. Natl. Acad. Sci. 112(44), E6058–E6067 (2015)
Kwak, N.S., Müller, K.R., Lee, S.W.: A convolutional neural network for steady state visual evoked potential classification under ambulatory environment. PLoS ONE 12(2), e0172578 (2017)
Zhang, R., et al.: The effect of stimulus number on the recognition accuracy and information transfer rate of SSVEP-BCI in augmented reality. J. Neural Eng. 19(3), 036010 (2022)
Pan, Y., Chen, J., Zhang, Y.: A Survey of deep learning-based classification methods for steady-state visual evoked potentials. Brain-Apparat. Commun. J. Bacomics 2(1), 2181102 (2023). https://doi.org/10.1080/27706710.2023.2181102
Lin, Z., Zhang, C., Wu, W., Gao, X.: Frequency recognition based on canonical correlation analysis for SSVEP-based BCIs. IEEE Trans. Biomed. Eng. 53(12), 2610–2614 (2006)
Zhang, Y., Xu, P., Cheng, K., Yao, D.: Multivariate synchronization index for frequency recognition of SSVEP-based brain-computer interface. J. Neurosci. Methods 221, 32–40 (2014)
Chen, Y., Yang, C., Ye, X., Chen, X., Wang, Y., Gao, X.: Implementing a calibration-free SSVEP-based BCI system with 160 targets. J. Neural Eng. 18(4), 046094 (2021)
Nakanishi, M., Wang, Y., Chen, X., Wang, Y.T., Gao, X., Jung, T.P.: Enhancing detection of SSVEPs for a high-speed brain speller using task-related component analysis. IEEE Trans. Biomed. Eng. 65(1), 104–112 (2017)
Liu, B., Chen, X., Shi, N., Wang, Y., Gao, S., Gao, X.: Improving the performance of individually calibrated SSVEP-BCI by task-discriminant component analysis. IEEE Trans. Neural Syst. Rehabil. Eng. 29, 1998–2007 (2021)
Chen, J., Zhang, Y., Pan, Y., Xu, P., Guan, C.: A Transformer-based deep neural network model for SSVEP classification. arXiv preprint arXiv:2210.04172 (2022)
Pan, Y., Chen, J., Zhang, Y., Zhang, Y.: An efficient CNN-LSTM network with spectral normalization and label smoothing technologies for SSVEP frequency recognition. J. Neural Eng. 19(5), 056014 (2022)
Luo, R., Xu, M., Zhou, X., Xiao, X., Jung, T.P., Ming, D.: Data augmentation of SSVEPs using source aliasing matrix estimation for brain-computer interfaces. IEEE Trans. Biomed. Eng. 70, 1775–1785 (2022)
Li, R., Wang, L., Suganthan, P., Sourina, O.: Sample-based data augmentation based on electroencephalogram intrinsic characteristics. IEEE J. Biomed. Health Inform. 26(10), 4996–5003 (2022)
Aznan, N.K.N., Atapour-Abarghouei, A., Bonner, S., Connolly, J.D., Al Moubayed, N., Breckon, T.P.: Simulating brain signals: creating synthetic EEG data via neural-based generative models for improved ssvep classification. In: 2019 International Joint Conference on Neural Networks (IJCNN), pp. 1–8. IEEE, Budapest, Hungary (2019)
Pan, Y., Li, N., Zhang, Y.: Short-time SSVEP data extension by a novel generative adversarial networks based framework. arXiv preprint arXiv:2301.05599 (2023)
Wang, Y., Chen, X., Gao, X., Gao, S.: A benchmark dataset for SSVEP-based brain-computer interfaces. IEEE Trans. Neural Syst. Rehabil. Eng. 25(10), 1746–1752 (2016)
Liu, B., Huang, X., Wang, Y., Chen, X., Gao, X.: Beta: a large benchmark database toward SSVEP-BCI application. Front. Neurosci. 14, 627 (2020)
Tanaka, H., Katura, T., Sato, H.: Task-related component analysis for functional neuroimaging and application to near-infrared spectroscopy data. Neuroimage 64, 308–327 (2013)
Chen, X., Wang, Y., Gao, S., Jung, T.P., Gao, X.: Filter bank canonical correlation analysis for implementing a high-speed SSVEP-based brain-computer interface. J. Neural Eng. 12(4), 046008 (2015)
Wong, C.M., et al.: Learning across multi-stimulus enhances target recognition methods in SSVEP-based BCIs. J. Neural Eng. 17(1), 016026 (2020)
Sun, Q., Chen, M., Zhang, L., Li, C., Kang, W.: Similarity-constrained task-related component analysis for enhancing SSVEP detection. J. Neural Eng. 18(4), 046080 (2021)
Wang, Y., Wang, R., Gao, X., Hong, B., Gao, S.: A practical VEP-based brain-computer interface. IEEE Trans. Neural Syst. Rehabil. Eng. 14(2), 234–240 (2006)
Waytowich, N., et al.: Compact convolutional neural networks for classification of asynchronous steady-state visual evoked potentials. J. Neural Eng. 15(6), 066031 (2018)
Acknowledgements
This work was supported in part by the National Natural Science Foundation of China under Grant No.62076209.
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Pan, Y., Li, N., Xiong, L., Luo, Y., Zhang, Y. (2024). SSVEP Data Augmentation Based on Filter Band Masking and Random Phase Erasing. In: Luo, B., Cheng, L., Wu, ZG., Li, H., Li, C. (eds) Neural Information Processing. ICONIP 2023. Communications in Computer and Information Science, vol 1963. Springer, Singapore. https://doi.org/10.1007/978-981-99-8138-0_38
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DOI: https://doi.org/10.1007/978-981-99-8138-0_38
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