EEG-based motor imagery analysis using weighted wavelet transform features.
In this study, an electroencephalogram (EEG) analysis system for single-trial classification of motor imagery (MI) data is proposed. Feature extraction in brain-computer interface (BCI) work is an important task that significantly affects the success of brain signal classification. The continuous wavelet transform (CWT) is applied together with Student's two-sample t-statistics for 2D time-scale feature extraction, where features are extracted from EEG signals recorded from subjects performing left and right MI. First, we utilize the CWT to construct a 2D time-scale feature, which yields a highly redundant representation of EEG signals in the time-frequency domain, from which we can obtain precise localization of event-related brain desynchronization and synchronization (ERD and ERS) components. We then weight the 2D time-scale feature with Student's two-sample t-statistics, representing a time-scale plot of discriminant information between left and right MI. These important characteristics, including precise localization and significant discriminative ability, substantially enhance the classification of mental tasks. Finally, a correlation coefficient is used to classify the MI data. Due to its simplicity, it will enable the performance of our proposed method to be clearly demonstrated. Compared to a conventional 2D time-frequency feature and three well-known time-frequency approaches, the experimental results show that the proposed method provides reliable 2D time-scale features for BCI classification.
Hsu
Wei-Yen
WY
Department of Computer Science and Information Engineering, National Cheng Kung University, Tainan, Taiwan, ROC.
Sun
Yung-Nien
YN
D019540:Area Under Curve; D001921:Brain / Q000502:physiology*; D004569:Electroencephalography / Q000379:methods*; D006801:Humans; D007092:Imagination / Q000502:physiology*; D016571:Neural Networks, Computer; D012815:Signal Processing, Computer-Assisted; D013997:Time Factors; D014584:User-Computer Interface