Abstract
Event-Related Potentials (ERPs) based binary BCI systems help enable users to control external devices through brain signals responding to stimulus. However, the external properties of the auditory or visual stimuli in the typical oddball-paradigm are loud and large for a user, which often brings psychological discomfort. In this study, we proposed novel non-oddball BCI paradigms where the intensity of external properties is greatly minimized while maintaining the system performance. To compensate for the loss of accuracy from the diminutive stimulus, users were instructed to generate discriminant ERP responses by performing a voluntary mental task. As the result, task-relevant endogenous components were investigated by the certain mental task and greatly enhanced system performance. The decoding accuracies of proposed CNN with data augmentation technique were 77.8% and 76.7% for the non-oddball visual and auditory paradigms, respectively, which significantly outperformed the linear classifier model. These results open up novel avenues for practical ERP systems, which could increase the usability of current brain-computer interfaces remarkably.
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Acknowledgments
This work was supported by Faculty Development Competitive Research Grant Program (No. 080420FD1909) at Nazarbayev University and by Institute for Information & communications Technology Planning & Evaluation (IITP) grant funded by the Korea government (MSIT) (No. 2017-0-00451).
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Saparbayeva, M., Shomanov, A., Lee, MH. (2021). A Novel Binary BCI Systems Based on Non-oddball Auditory and Visual Paradigms. In: Mantoro, T., Lee, M., Ayu, M.A., Wong, K.W., Hidayanto, A.N. (eds) Neural Information Processing. ICONIP 2021. Lecture Notes in Computer Science(), vol 13110. Springer, Cham. https://doi.org/10.1007/978-3-030-92238-2_1
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