Non stationary, non-linear, and weak signals are difficult to analyze and process. A novel signal processing method based on empirical mode decomposition (EMD) and learning vector quantization (LVQ) neural network is proposed and applied in the field of biological signal processing (left and right hands move imagery electroencephalogram (EEG) signal). Firstly, EMD is used to decompose EEG signal. Secondly, the major intrinsic mode function components are extracted and their mean absolute values are calculated as the features. Finally, LVQ is used to finish the classification task. Then the results are compared with the support vector machine and error back propagation neural network classification algorithms. The experimental results show that the classification accuracy rate of the proposed algorithm reaches 87%. Compared to the other two contrast algorithms, the new algorithm has better performance in the specific signal processing field and thus has high reference and research value.