Abstract
Brain computer interfaces provide a novel channel for the communication between brain and output devices. The effectiveness of the brain computer interface is based on the classification accuracy of single trial brain signals. The common spatial pattern (CSP) algorithm is believed to be an effective algorithm for the classification of single trial brain signals. As the amplitude feature for spatial projection applied by this algorithm is based on a broad frequency bandpass filter (mainly 5–30 Hz) in which the frequency band is often selected by experience, the CSP is sensitive to noise and the influence of other irrelevant information in the selected broad frequency band. In this paper, to improve the CSP, a novel relevant feature integration and extraction algorithm is proposed. Before projecting, we integrated the motor relevant information to suppress the interference of noise and irrelevant information, as well as to improve the spatial difference for projection. The algorithm was evaluated with public datasets. It showed significantly better classification performance with single trial electroencephalography (EEG) data, increasing by 6.8% compared with the CSP.
Highlights
Brain-computer interface (BCI) is a way of communication that aims to provide a communication path between humans and computers
They showed that the Spectral Component Common Spatial Pattern (SCCSP) outperformed common spatial pattern (CSP), Independent Components Analysis (ICA)-CSP, Common Sparse SpectralSpatial Pattern (CSSSP), Bilinear Common Spatial Pattern (BCSP) and Analytic Common Spatial Patterns (ACSP), achieving 6.8, 3.5, 11.5, 26, and 15.5% higher average classification accuracy than these algorithms, respectively
The paired ttest showed that the better performances of SCCSP over CSP (p < 0.05), ICA-CSP (p < 0.05), CSSSP (p < 0.05), BCSP (p < 0.001) and ACSP (p < 0.001) were significant
Summary
Brain-computer interface (BCI) is a way of communication that aims to provide a communication path between humans and computers. It provides a new approach to further improve the classification performance of the motor-imagery-based BCIs. To feature optimize, it focuses on the changes of the amplitude spectrum during motor imagery, and utilizes Independent Components Analysis (ICA) to extract the components from multi-channel amplitude spectrum with the aim of separating motor-relevant and irrelevant information from obscure EEG amplitude features applied by CSP.
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