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>>23 > A. > Result I: > Predictive Model for SSVEP Magnitude Variation The purpose of this study is to select the most appropriate model to predict SSVEP magnitude variation. > Six predictive models are compared here; Poly poly2, Poly poly3, RF poly2, RF poly3, NN(GRUs) poly2 and NN(GRUs) poly3. > As shown in Table I, the mean of MSE from all predictive models is not significantly different. > However, there is a statistical difference in the mean comparison of the computational-time prediction. > One-way repeated measures ANOVA with the Greenhouse-Geisser correction reported F(1.377, 12.395) = 383.877, p<0.01. > Moreover, the Bonferroni correction and pairwise comparison presented the computational-time prediction for both textitRF poly2 and RF poly3 models as significantly lower than the other models, p<0.01. > However, RF poly2 was selected for the rest of the study since it has less complexity in polynomial degrees. > In order to obtain qualitative results, the predicted signals were plotted per time step for each experiment condition as shown in Figure 5. > > B. > Result II: > Brain-Controlled Robotic Simulator > 1) > Trade-off between processing window length and smooth movement: Window length plays an important role in online brain-controlled applications. > The mean of the average speed and the mean of the box deviation for varying window lengths from one to five seconds are compared in this subsection. > > > Page 7 > AUTHOR et al.: PREPARATION OF PAPERS FOR IEEE TRANSACTIONS AND JOURNALS (FEBRUARY 2017) > 7
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