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In this work, we study feature reconstruction and image reconstruction is carried out separately.
In this study, feature vectors for EEG signals were obtained by using DWT method.
In this study, feature selection is used mainly for the simplification of models for better feature interpretation, and for the reduction of overfitting.
In this study, feature warping (FW) and cepstral mean and variance normalization (CMVN) over a sliding window are used [39, 40] to reduce the noise and handset effects and mitigate linear channel effects; this gives improvements and robustness to SIA [6].
The overall goal of the present series of experiments was to study feature selection in multidimensional stimuli, with targets defined by a combination of three distinct characteristics.
However, this study feature was considered an essential safeguard against confounding by phenacetin use.
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