An entropy fusion method for feature extraction of EEG

Feature extraction is a vital part in EEG classification. Among the various feature extraction methods, entropy reflects the complexity of the signal. Different entropies reflect the characteristics of the signal from different views. In this paper, we propose a feature extraction method using the fusion of different entropies. The fusion can be a more complete expression of the characteristic of EEG. Four entropies, namely a measure for amplitude based on Shannon entropy, a measure for phase synchronization based on Shannon entropy, wavelet entropy and sample entropy, are firstly extracted from the collected EEG signals. Support vector machine and principal component analysis are then used for classification and dimensionality reduction, respectively. We employ BCI competition 2003 dataset III to evaluate the method. The experimental results show that our method based on four entropies fusion can achieve better classification performance, and the accuracy approximately reaches 88.36 %. Finally, it comes to the conclusion that our method has achieved good performance for feature extraction in EEG classification.