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In this work the self-organizing fuzzy neural network (SOFNN) is employed to create an accurate and easily calibrated approach to multiple-step-ahead prediction for the NN5 forecasting competition 2008. The competition dataset consists of 111 daily empirical time series of cash-machine withdrawals. The objective for the competition was to forecast future transactions up to 56 days ahead with the highest...
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...
The accurate discrimination of EEG times-series is a challenging problem and has become a topic of prominent research interest, given the extent of the research activity in the area of brain-computer interface (BCI) technology. Many signal processing algorithms involving preprocessing, feature extraction/selection, and classification have been deployed and yet, the most appropriate and robust solutions...
The issue of subject-specific parameter selection in an electroencephalogram (EEG)-based brain-computer interface (BCI) is tackled in this paper. Hjorth- and Barlow-based feature extraction procedures (FEPs) are investigated along with linear discriminant analysis (LDA) for classification. These are well-known nonparametric FEPs but their simplicity prevents them from matching the performance of more...
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...
This paper presents a preprocessing procedure for improving the separability of electroencephalogram (EEG) signals recorded from subjects for a right/left motor imagery based brain-computer interface (BCI). The EEG data is preprocessed utilizing a recently proposed time-series-prediction (TSP) technique. Two neural networks (NNs) are trained to perform one-step-ahead predictions of the EEG time-series...
This paper presents a novel feature extraction procedure (FEP) for extracting features from the electroencephalogram (EEG) recorded from subjects producing right and left motor imagery. Four self-organizing fuzzy neural networks (SOFNNs) are coalesced to perform one-step-ahead predictions for the EEG time series data. Features are derived from the mean squared error (MSE) in prediction or the mean...
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