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)

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 extrac...