An effective feature extraction method by power spectral density of EEG signal for 2-class motor imagery-based BCI

• Published in: Medical & biological engineering & computing Vol. 56; no. 9; pp. 1645 - 1658
• Main Authors: Kim, Chungsong, Sun, Jinwei, Liu, Dan, Wang, Qisong, Paek, Sunggyun
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.09.2018; Springer Nature B.V
• Subjects: Biomedical and Life Sciences; Biomedical Engineering and Bioengineering; Biomedicine; Computer Applications; Density; EEG; Electroencephalography; Feature extraction; Frequency dependence; Human Physiology; Imagery; Imaging; Mathematical analysis; Mental task performance; Noise intensity; Original Article; Radiology; Spectra; Brain-computer interface (BCI); Feature extraction; 2-class motor imagery; Electroencephalogram (EEG); Power spectral density (PSD)
• ISSN: 0140-0118; 1741-0444; 1741-0444
• DOI: 10.1007/s11517-017-1761-4

EEG signals have weak intensity, low signal-to-noise ratio, non-stationary, non-linear, time-frequency-spatial characteristics. Therefore, it is important to extract adaptive and robust features that reflect time, frequency and spatial characteristics. This paper proposes an effective feature extraction method WDPSD (feature extraction from the Weighted Difference of Power Spectral Density in an optimal channel couple) that can reflect time, frequency and spatial characteristics for 2-class motor imagery-based BCI system. In the WDPSD method, firstly, Power Spectral Density (PSD) matrices of EEG signals are calculated in all channels, and an optimal channel couple is selected from all possible channel couples by checking non-stationary and class separability, and then a weight matrix which reflects non-stationary of PSD difference matrix in selected channel couple is calculated; finally, the robust and adaptive features are extracted from the PSD difference matrix weighted by the weight matrix. The proposed method is evaluated from EEG signals of BCI Competition IV Dataset 2a and Dataset 2b. The experimental results show a good classification accuracy in single session, session-to-session, and the different types of 2-class motor imagery for different subjects.