An improved version of local activities estimation to enhance motor imagery classification. (April 2021)

• Record Type: Journal Article
• Authors: Togha, Mohammad Mahdi; Salehi, Mohammad Reza; Abiri, Ebrahim
• Is Part Of: Biomedical signal processing and control. Volume 66(2021)
• Publication Date: 2021-04
• Subjects: Local activities estimation -- Common spatial pattern -- Brain-computer interface (BCI) -- Motor imagery (MI) -- Calibration time reduction; Signal processing -- Periodicals; Biomedical engineering -- Periodicals; Signal Processing, Computer-Assisted -- Periodicals; Image Processing, Computer-Assisted -- Periodicals; Biomedical Engineering -- Periodicals; 610.28
• DOI: 10.1016/j.bspc.2021.102485
• Languages: English
• ISSNs: 1746-8094

Abstract

Highlights: The combination of LAE and CSP performs better than either method. LAE-CSP achieves a mean accuracy of 78.52 % over 28 subjects using 10 training trials. CSP, FBRCSP, EA-CSP and LAE were compared with LAE-CSP using 4 different datasets. Abstract: Objective: The common spatial pattern (CSP) and its variants are popular in the EEG-based motor imagery BCIs. However, this method has some drawbacks, especially when a few labeled samples are available. The local activities estimation (LAE) method works well with small training sets, but it is more sensitive to the position of the electrodes. Here, we suggest a combination of the LAE and CSP, namely LAE-CSP, which performs better than both methods. Methods: In LAE-CSP, the EEG signal passes through regularized CSP and LAE spatial filters after band-pass filtering and then the data dimension are reduced based on the physiological data. Afterwards, the features are extracted using fast Fourier transform (FFT). In this work, CSP, FBRCSP, EA-CSP, LAE and LAE-CSP, methods were evaluated and compared. Three sets of motor imagery data from BCI competition III and IV along with Cho et al. dataset, including EEG signals from 28 subjects were used in this study. For each dataset, the training set were selected in 21 different sizes. Results: LAE-CSP performs better than all tested methods. Particularly it has good performance using only ten labeled samples per class, with an average accuracy of almost 80 %. Conclusions: LAE-CSP reliably enhances the performance of the motor imagery-based brain-computer interfaces. Significance: The LAE-CSP takes advantage of the LAE and CSP and compensates for the drawbacks of both methods.