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

Ao Chen, Dayang Sun, Xin Gao, Dingguo Zhang

Research output: Contribution to journalArticlepeer-review


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.

Original languageEnglish
Article number108619
JournalComputers in Biology and Medicine
Early online date20 May 2024
Publication statusE-pub ahead of print - 20 May 2024


  • Brain-computer interfaces
  • Electroencephalography
  • Feature extraction
  • Machine learning
  • Motor imagery
  • Spectral subtraction

ASJC Scopus subject areas

  • Health Informatics
  • Computer Science Applications

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