Abstract
This paper presents a methodology to classify motor imagery by extracting fuzzy rules based on the neural network with weighted fuzzy membership functions (NEWFM) and twenty-four numbers of input features that are extracted by wavelet-based features. This paper consists of three steps to classify motor imagery. In the first step, wavelet transform is performed to filter noises from signals. In the second step, twenty-four numbers of input features are extracted by wavelet-based features from filtered signals by wavelet transform. In the final step, NEWFM classifies motor imagery using twenty-four numbers of input features that are extracted in the second step. In this paper, twenty-four numbers of input features are selected for generating the fuzzy rules to classify motor imagery. NEWFM is tested on the Graz BCI datasets that were used in the BCI Competitions of 2003. The accuracy of NEWFM is 83.51%.
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Lee, SH., Lim, J.S., Shin, DK. (2010). Extracting Fuzzy Rules to Classify Motor Imagery Based on a Neural Network with Weighted Fuzzy Membership Functions. In: Zavoral, F., Yaghob, J., Pichappan, P., El-Qawasmeh, E. (eds) Networked Digital Technologies. NDT 2010. Communications in Computer and Information Science, vol 87. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-14292-5_2
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DOI: https://doi.org/10.1007/978-3-642-14292-5_2
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