Faster Self-Organizing Fuzzy Neural Network Training and a Hyperparameter Analysis for a Brain-Computer Interface

This paper introduces a number of modifications to the learning algorithm of the self-organizing fuzzy neural network (SOFNN) to improve computational efficiency. It is shown that the modified SOFNN compares favorably to other evolving fuzzy systems in terms of accuracy and structural complexity. An analysis of the SOFNNs effectiveness when applied in an electroencephalogram (EEG)-based brain-computer interface (BCI) involving the neural-time-series-prediction-preprocessing (NTSPP) framework is also presented, where a sensitivity analysis (SA) of the SOFNN hyperparameters was performed using EEG data recorded from three subjects during left/right motor imagery-based BCI experiments. The aim of this one-time SA was to eliminate the need to choose subject- and signal-specific hyperparameters for the SOFNN and thus apply the SOFNN in the NTSPP framework as a parameterless self-organizing framework for EEG preprocessing. The results indicate that a general set of NTSPP parameters chosen via the SA provide the best results when tested in a BCI system. Therefore, with this general set of SOFNN parameters and its self-organizing structure, in conjunction with parameterless feature extraction and linear discriminant classification, a fully parameterless BCI which lends itself well to autonomous adaptation is realizable.