Abstract: The magnetoenephalography(MEG)signals have higher spatiotemporal resolution than EEG signals， which can be used as input signals to build braincomputer interface(BCI)system. Feature extraction and classification methods of the MEG signals were introduced. Firstly， the MEG signals were preprocessed， and then time domain features were extracted. Finally， Fisher linear discriminant analysis(LDA)was used to classify the MEG signals. This algorithm was used to the data set Ⅲ of 2008 BCI competition which was a typical MEGbased BCI system. The offline analysis results showed that high classification accuracy of 5946% and 4324% for two subjects(subject S1 and subject S2)could be obtained using this proposed algorithm. This algorithm is more efficient and simpler than others， which can be regarded as a good reference.

Key words: magnetoenephalography(MEG), braincomputer interface, linear discriminant analysis, feature extraction, classification