A feature extraction method based on dictionary learning for EEG

For decades, it has been widely used to extract EEG features on every single trial, while in this article, features are extracted based on one fixed dictionary basis. Here, by designing a feature extraction method applying dictionary learning on EEG signals and by using the BCI competition EEG data of two classes, we show that the degree of every used dictionary component related to task state and relaxed state are different and could be used as the feature of EEG. What's more, we use Bayesian classifier to classify our features compared with wavelet features and find that our accuracy is a lot higher than wavelet.