Publication

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.

DOI: https://doi.org/10.1016/j.trip.2020.100190.

DOI: 10.5220/0008969702080215

DOI: https://doi.org/10.1088/2057-1976/ab87e6

DOI: https://doi.org/10.1155/2020/7985010.

DOI: https://doi.org/10.1109/THMS.2020.2983661.