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• W3110772761 abstract "Most brain-computer interfaces (BCIs) utilize univariate features (i.e., power spectrum or amplitude) for motor imagery (MI) pattern recognition, while less attention has been paid on multivariate analysis that considers information flow between various brain areas through brain connectivity estimations. Most recently, researches have proved that connectivity features were able to characterize different MI tasks. In this study, we investigated the performance of functional connectivity features measured by phase lag index (PLI), weighted phase lag index(wPLI) and phase-locking value(PLV) on MI classification. The widely-used filter-bank common spatial pattern (FBCSP) approach was employed here for performance assessment. A linear support vector machine was trained to classify different MI tasks using two publicly-available datasets from BCI Competition III and IV. The classification results showed that connectivity features achieved classification accuracy >85% in most cases and PLI outperformed all other methods including FBCSP. Our work suggested that functional connectivity features could be utilized as a powerful tool for recognizing different motor intention." @default.
• W3110772761 created "2020-12-21" @default.
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• W3110772761 date "2020-10-11" @default.
• W3110772761 modified "2023-09-30" @default.
• W3110772761 title "Functional Connectivity for Motor Imaginary Recognition in Brain-computer Interface" @default.
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• W3110772761 doi "https://doi.org/10.1109/smc42975.2020.9283075" @default.
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