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Recent research has shown that neural networks (NNs) or self-organizing fuzzy NNs (SOFNNs) can enhance the separability of motor imagery altered electroencephalogram (EEG) for brain-computer interface (BCI) systems. This is achieved via the neural-time-series-prediction-preprocessing (NTSPP) framework where SOFNN prediction models are trained to specialize in predicting the EEG time-series recorded from different EEG channels whilst subjects perform various mental tasks. Features are extracted from the predicted signals produced by the SOFNN and it has been shown that these features are easier to classify than those extracted from the original EEG. Previous work was based on a two class BCI. This paper presents an analysis of the NTSPP framework when extended to operate in a multiclass BCI system. In mutliclass systems normally multiple EEG channels are used and a significant amount of subject-specific parameters and EEG channels are investigated. This paper demonstrates how the SOFNN-based NTSPP, tested in conjunction with three different feature extraction procedures and different linear discriminant and support vector machine (SVM) classifiers, is effective in improving the performance of a multiclass BCI system, even with a low number of standardly positioned electrodes and no subject-specific parameter tuning.