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>>15 > After step 1 was completed, 48 blocks of data were obtained for the following structure: 4 conditions × 48 blocks × 750 data points or features. > Then, for step 2 , each data point was squared to obtain the input signals of this subject. > The second set of data consisted of target signals (b). > Starting from 3 , the average value of each block was calculated from the input signals. > Hence, 48 values were obtained for each condition. > After that, in step 4 , curve fittings were performed with polynomial functions (either a quadratic function (poly2) or a cubic function (poly3)) for each condition. > Finally, the target signals were 4 conditions × 48 target values (four curves with 48 points each). > After the data preparation was completed, the input signals and target signals of each subject were used for SSVEP amplitude prediction using the following approaches. > 2) > Neural Networks Approach (NN): A recurrent neural network (RNN) is extended from a conventional feed-forward neural network and has the ability to extract essential features from time series data, such as EEG, due to its recurrent hidden state. > Their activation at each time step is calculated using data in the previous step. > The proposed NN model in this study starts with a layer of Gated Recurrent Units (GRUs); one of the recurrent unit types in RNNs [15]. > Its update gate makes the model recall the existence of a specific feature in the input stream for a longer series than conventional RNNs. > Subsequently, a fully connected (FC) layer was used due to its appropriateness for time series data prediction.
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