Abstract: Motor imaging EEG signals are typical non-linear and non-stationary, thus the traditional classification method based on single feature extraction is difficult to achieve better classification performance. Aiming at this problem, the Fractional Fourier Transform (FrFT) is introduced into the feature extraction process of EEG signals. Firstly, FrFT is used to analyze signals, the useful information is extracted from different dimensions while expanding the feature domain, and the feature vectors is formed. Then Support Vector Machine (SVM) classifier is used to classify the proposed feature vectors. Finally, the experiment is carried out using Graz data. The experimental results show that the proposed method can achieve up to 92.57% correct classification results, which is significantly higher than the traditional classification method using single feature extraction.

Key words: classification of EEG signals, fractional Fourier transform, pattern classification, feature extraction