A novel feature extraction method PSS-CSP for binary motor imagery – based brain-computer interfaces

In order to improve the performance of binary motor imagery (MI) – based brain-computer interfaces (BCIs) using electroencephalography (EEG), a novel method (PSS-CSP) is proposed, which combines spectral subtraction with common spatial pattern. Spectral subtraction is an effective denoising method which is initially adopted to process MI-based EEG signals for binary BCIs in this work. On this basis, we proposed a novel feature extraction method called power spectral subtraction-based common spatial pattern (PSS-CSP) , which calculates the differences in power spectrum between binary classes of EEG signals and uses the differences in the feature extraction process. Additionally, support vector machine (SVM) algorithm is used for signal classification. Results show the proposed method (PSS-CSP) outperforms certain existing methods, achieving a classification accuracy of 76.8% on the BCIIV dataset 2b, and 76.25% and 77.38% on the OpenBMI dataset session 1 and session 2, respectively. •A novel feature extraction method was used to process binary motor imagery signals.•This paper studies spectral subtraction for denoising motor imagery signals.•The effectiveness of the method is established using two publically BCI datasets.•The method improves the classification accuracy in comparison with other methods.