Recently, visual perception (VP) and visual imagery (VI) paradigms are investigated in several brain-computer interface (BCI) studies. VP is defined as a changing of brain signals when perceiving visual information and VI is defined as a changing of brain signals when  memorizing visual information  . These paradigms could be alternatives to the previous visual-based paradigms which have limitations such as fatigue and low information transfer rates (ITR). In this study, we analyzed VP and VI to investigate the possibility to control BCI. First, we conducted a time-frequency analysis with event-related spectral perturbation to represent the reliability of the brain signal while performing VP and VI . In addition, two types of decoding accuracies were obtained with the convolutional neural network to verify whether the brain signals can be distinguished from each class in the VP and whether they can be differentiated with VP and VI paradigms. As a result, we obtained a high performance of 43.00\% (chance level is 16.67\%) in the 6-class classification (VP) of subject 1. The average performance was 32.56\% . Also, the binary classification performance which classifies two paradigms was 90.16\% . These results indicate that the use of the proposed method is beneficial for the classification and prediction of the brain states from visual tasks (i.e. VP and VI) when developing comfortable BCI systems .
Recently, visual perception (VP) and visual imagery (VI) paradigms are investigated in several brain-computer interface (BCI) studies. VP and VI are defined as a changing of brain signals when perceiving and memorizing visual information , respectively . These paradigms could be alternatives to the previous visual-based paradigms which have limitations such as fatigue and low information transfer rates (ITR). In this study, we analyzed VP and VI to investigate the possibility to control BCI. First, we conducted a time-frequency analysis with event-related spectral perturbation . In addition, two types of decoding accuracies were obtained with convolutional neural network to verify whether the brain signals can be distinguished from each class in the VP and whether they can be differentiated with VP and VI paradigms. As a result, the 6-class classification performance in VP was 32.56\% and the binary classification performance which classifies two paradigms was 90.16\% .
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is defined as a changing of brain signals when perceiving visual information and VI is
and VI are
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perceiving and
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, respectively
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to represent the reliability of the brain signal while performing VP and VI
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the
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we obtained a high performance of 43.00\% (chance level is 16.67\%) in
R
(VP) of subject 1. The average performance
performance in VP
R
. Also,
and
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. These results indicate that the use of the proposed method is beneficial for the classification and prediction of the brain states from visual tasks (i.e. VP and VI) when developing comfortable BCI systems