چكيده به لاتين
The brain exhibits organized fluctuations of neural activity, even in the absence of tasks or sensory input. This activity is known as “Ongoing”, “Spontaneous” and “prestimulus”. According to studies, brain responses to a stimulus can be predicted from the oscillations of different frequency bands of the EEG signal in the pre-stimulus state. Spontaneous brain activity, sometimes referred to as background noise, appears to be the main cause of high trial-to-trial changes in cortical responses to a single stimulus in brain recordings. In general, the purpose of this study is to use the information in the EEG signal rythms in the pre-stimulus state, to predict the correct or incorrect response of the subject as well as visual awareness after observing the stimulus. This study uses data from a research by Benwell et al. (2017) in which visual stimuli including black and white Gaussian patches with 6 contrast levels of 25, 50 and 75% for two modes, lighter and darker than the background, Shown to 14 participants, they should respond that the stimulus shown was darker or lighter than the gray background. As a criteria of the user's visual awareness, participants were asked to assign a number between 0 and 3 to their confidence in seeing the stimulus. In this study, the logarithm of common frequency fluctuations of delta, theta, alpha, beta and gamma in five consecutive half-second intervals before stimulus observation, on 61 electroencephalogram channels, has been used to predict the user's correct or incorrect response to the stimulus being darker or lighter than the background, as well as the level of visual awareness of the stimulus. The t-test and Sequential Forward Feature selection were used to select the features that show significant differences in the user's correct and incorrect answers in the train data. In the model predicting the user's correct or incorrect response and in the model predicting the user's visual awareness of the stimulus the Decision tree classifier was used. The results showed that, the user's response was predicted in -75 contrast with 73% accuracy and in +75 contrast with 76.55% accuracy. Also, the results of predicting the level of visual awareness, Which was obtained by calculating the classification accuracy of 4 classes, showed that in total in all 6 levels of contrast, classification accuracy above 40% was obtained, which shows prediction,was performed, using the feature Extracted from the interval before the stimulus was applied. According to the results, it was observed that the information of all frequency bands and information of all time intervals before applying the stimulus, was effective in predicting user's performance.
