Enhancing Autonomy and Computational Efficiency of the Self-Organizing Fuzzy Neural Network for a Brain Computer Interface

This paper presents a number of enhancements to the self-organizing fuzzy neural network (SOFNN). Firstly, the SOFNN is described and a modification to the learning algorithm to improve computational efficiency is introduced. Secondly, a sensitivity analysis (SA) of the predefined SOFNN parameters is presented using electroencephalogram (EEG) data recorded from three subjects during left/right motor imagery-based brain-computer interface (BCI) experiments. This SA was carried out to determine if a general set of parameters could be used for predicting various non-stationary EEG time-series dynamics for multiple subjects. The SOFNN modifications significantly enhance computational efficiency and the SA results suggest that it may be possible to select a general set of parameters for different motor imagery-based EEG signals thus potentially enhancing the SOFNNs autonomy for application in a BCI.