Abstract
Brain-computer interfaces (BCIs) translate brain activity into computer commands opening a novel non-muscular channel for communication and control. Before BCIs can be used effectively, they require extensive calibration of their machine learning algorithms using labeled training data. This tedious calibration process is not only time-consuming and costly but also restricts the exploration and optimization of stimulus parameters that could greatly enhance BCI performance. To overcome the challenge of acquiring large training datasets, a simulation framework was developed to eliminate the need for recording calibration data. Unlike previous studies, this simulation framework incorporates a biologically plausible forward model of the code-modulated visual evoked potential (c-VEP). By utilizing synthetic data generated by this improved simulation framework, an offline study was conducted to systematically compare five different stimulus conditions: almost perfect autocorrelation (APA) sequence, de Bruijn sequence, Golay sequence, Gold code, and m-sequence. The results of the study revealed that the Golay sequence achieved the highest grand average performance, followed by the APA sequence, m-sequence, Gold code, and finally the de Bruijn sequence. Furthermore, when the stimulus sequence was optimized for individual participants, typically the Golay and APA sequences exhibited the highest classification accuracy, leading to a significant improvement in overall accuracy as compared to using the Golay sequence for all participants. This research represents an important initial step towards optimizing stimulus parameters for BCIs using simulated data. This approach has the potential to accelerate the development and optimization of BCIs, resulting in more effective applications that can be tailored to individual users.
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Notes
- 1.
PyNT can be installed from source: https://gitlab.socsci.ru.nl/jthielen/pynt.
References
Bin, G., Gao, X., Wang, Y., Hong, B., Gao, S.: VEP-based brain-computer interfaces: time, frequency, and code modulations. IEEE Comput. Intell. Mag. 4(4), 22–26 (2009)
Capilla, A., Pazo-Alvarez, P., Darriba, A., Campo, P., Gross, J.: Steady-state visual evoked potentials can be explained by temporal superposition of transient event-related responses. PLoS ONE 6(1), e14543 (2011)
Eilts, H., Putze, F.: Is that real? A multifaceted evaluation of the quality of simulated EEG signals for passive BCI. In: 2022 IEEE International Conference on Systems, Man, and Cybernetics (SMC), pp. 2639–2644. IEEE (2022)
Lindgren, J.T., Merlini, A., Lecuyer, A., Andriulli, F.P.: simBCI-a framework for studying BCI methods by simulated EEG. IEEE Trans. Neural Syst. Rehabil. Eng. 26(11), 2096–2105 (2018)
Martínez-Cagigal, V., Thielen, J., Santamaría-Vázquez, E., Pérez-Velasco, S., Desain, P., Hornero, R.: Brain-computer interfaces based on code-modulated visual evoked potentials (c-VEP): a literature review. J. Neural Eng. 18, 061002 (2021)
Nagel, S., Dreher, W., Rosenstiel, W., Spüler, M.: The effect of monitor raster latency on VEPs, ERPs and brain-computer interface performance. J. Neurosci. Methods 295, 45–50 (2018)
Rezeika, A., Benda, M., Stawicki, P., Gembler, F., Saboor, A., Volosyak, I.: Brain-computer interface spellers: a review. Brain Sci. 8(4), 57 (2018)
Spüler, M., Walter, A., Rosenstiel, W., Bogdan, M.: Spatial filtering based on canonical correlation analysis for classification of evoked or event-related potentials in EEG data. IEEE Trans. Neural Syst. Rehabil. Eng. 22(6), 1097–1103 (2013)
Thielen, J., Marsman, P., Farquhar, J., Desain, P.: Re(con)volution: accurate response prediction for broad-band evoked potentials-based brain computer interfaces. In: Guger, C., Allison, B., Lebedev, M. (eds.) Brain-Computer Interface Research. SECE, pp. 35–42. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-64373-1_4
Thielen, J., van den Broek, P., Farquhar, J., Desain, P.: Broad-band visually evoked potentials: re(con)volution in brain-computer interfacing. PLoS ONE 10(7), e0133797 (2015)
Thielen, J., Marsman, P., Farquhar, J., Desain, P.: From full calibration to zero training for a code-modulated visual evoked potentials for brain-computer interface. J. Neural Eng. 18(5), 056007 (2021)
Torres, J.A.R., Daly, I.: How to build a fast and accurate code-modulated brain-computer interface. J. Neural Eng. 18, 046052 (2021)
Verbaarschot, C., et al.: A visual brain-computer interface as communication aid for patients with amyotrophic lateral sclerosis. Clin. Neurophysiol. 132(10), 2404–2415 (2021)
Wei, Q., et al.: A novel c-VEP BCI paradigm for increasing the number of stimulus targets based on grouping modulation with different codes. IEEE Trans. Neural Syst. Rehabil. Eng. 26(6), 1178–1187 (2018)
Yasinzai, M.N., Ider, Y.Z.: New approach for designing cVEP BCI stimuli based on superposition of edge responses. Biomed. Phys. Eng. Express 6(4), 045018 (2020)
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Thielen, J. (2023). Effects of Stimulus Sequences on Brain-Computer Interfaces Using Code-Modulated Visual Evoked Potentials: An Offline Simulation. In: Rojas, I., Joya, G., Catala, A. (eds) Advances in Computational Intelligence. IWANN 2023. Lecture Notes in Computer Science, vol 14135. Springer, Cham. https://doi.org/10.1007/978-3-031-43078-7_45
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