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DOI: 10.3389/fncom.2022.939202
Publication
Stawicki, P., Rezeika, A., & Volosyak, I. (2021, June). Effects of Training on BCI Accuracy in SSMVEP-based BCI. In International Work-Conference on Artificial Neural Networks (pp. 69-80). Springer, Cham.
DOI: 10.1007/978-3-030-85099-9_6
Abstract This paper investigates the effects of the training process on the classification accuracy for a steady-state motion visual evoked potentials (SSMVEP)-based brain-computer interface (BCI) paradigm. An SSMVEP-based BCI works similar to SSVEP with the main difference that the stimulus is smoothly changing its appearance, with a continuous motion, leading to less user fatigue. Typical SSMVEP classification utilises correlation algorithms to compare the incoming Electroencephalography (EEG) data with a sine-cosine template.
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Pedro Ribeiro, Anna Michel, Cristina Sylla, Ido Iurgel, Wolfgang Müller, Christian Ressel, Katharina Wennemaring : “The Impact of a Digitally-Augmented Reading Instruction on Reading Motivation and Comprehension of Third Graders.”, EAI International Conference on Design, Learning and Innovation. Springer, Cham, 2020
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DOI: 10.5220/0008969702080215
Volosyak, I., Rezeika, A., Benda, M., Gembler, F., and Stawicki, P. (2020) „Towards solving of the Illiteracy phenomenon for VEP-based brain-computer interfaces“. Biomedical Physics & Engineering Express, 6, 035034.
DOI: https://doi.org/10.1088/2057-1976/ab87e6
Gembler, F., Rezeika, A., Benda, M., and Volosyak, I. (2020). „Five Shades of Grey: Exploring Quintary m-Sequences for More User-Friendly c-VEP-Based BCIs.“ Computational Intelligence and Neuroscience.
DOI: https://doi.org/10.1155/2020/7985010.