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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.
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GENERIC COLORIZED JOURNAL, VOL. XX, NO. XX, XXXX 2017
(a) (b) (c) (d) (e) (f)
Fig.7:
Comparison of the VPs from three subjects.
The upper row (a)-(c) shows the VPs from the proposed SSVEP-BCI and the lower row (d)-(f) the VPs from the conventional SSVEP-BCI.
(a) (b)
Fig.8:
An example of a Pioneer P3DX robot movement with different paradigms.
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