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The analysis of electroencephalogram (EEG) signal is a low-cost and effective technique to examine electrical activity of the brain and diagnose brain diseases in the Brain Computer Interface (BCI) applications. Classification of EEG signals is an important task in BCI applications. This paper investigates two common methods of feature extraction on EEG signals, autoregressive (AR) model and approximate...
Spikes are an important part of information transmission between neurons in the biological brain. Biological evidence shows that information is carried in the timing of individual action potentials, rather than only the firing rate. Spiking neural networks are devised to capture more biological characteristics of the brain to construct more powerful intelligent systems. In this paper, we extend our...
The major issue of researchers in ANN field is the optimization of the training process including time cost and NN structure. In response to the long training time, Multi-Agent architecture of feed forward Flexible Neural Tree model (MAFNT) is introduced for parallelizing the NN training. Moreover, looking for the best topology of NN, for a given problem, accounts for the large feasible solutions...
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