Publication_Volosyak

DOI: 10.3390/brainsci12020234 Abstract This paper investigates the effects of the repetitive block-wise training process on the classification accuracy for a code-modulated visual evoked potentials (cVEP)-based brain–computer interface (BCI). The cVEP-based BCIs are popular thanks to their autocorrelation feature. The cVEP-based stimuli are generated by a specific code pattern, usually the m-sequence, which is phase-shifted between the individual targets. Typically, the cVEP classification requires a subject-specific template (individually created from the user’s own pre-recorded EEG responses to the same stimulus target), which is compared to the incoming electroencephalography (EEG) data, using the correlation algorithms.

DOI: 10.3389/fncom.2022.939202

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/10.1088/2057-1976/ab87e6

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

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

DOI: https://doi.org/10.3390/brainsci10040240.