Research on classification method of wavelet entropy and Fuzzy Neural Networks for motor imagery EEG

Since traditional electrical brain signal analysis is mostly qualitative, the development of new quantitative methods is crucial for restricting the subjectivity in the study of brain signals. These methods are particularly fruitful when they are strongly correlated with intuitive physical concepts that allow a better understanding of brain dynamics [1]. Here, new method based on wavelet entropy (WE) and Fuzzy Neural Networks (FNN) is applied. The WE carries information about the degree of order/disorder associated with a multi-frequency signal. In addition, the time evolution of the WE is calculated to give information about the dynamics in the EEG records. Within this framework, the major objective of the present work was to characterize in a quantitative way functional dynamics of order/disorder microstates in short duration EEG signals. This paper has tried to use fuzzy neural networks (FNN) as a classification method, which combines fuzzy membership and neural network frame, to drive the model automatically update in training and leaning processes, thus it can be applied in the simulations of complex conditions. In this way, EEG signals under motor imagery conditions were analyzed.