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  • Transfer learning improves MI BCI models classification accuracy in Parkinson's disease patients [Elektronski vir]
    Miladinović, Aleksandar ...
    Motor-Imagery based BCI (MI-BCI) neurorehabilitation can improve locomotor ability and reduce the deficit symptoms in Parkinson's Disease patients. Advanced Motor-Imagery BCI methods are needed to ... overcome the accuracy and time-related MI BCI calibration challenges in such patients. In this study, we proposed a Multi-session FBCSP (msFBCSP) based on inter-session transfer learning and we investigated its performance compared to the single-session based FBSCP. The main result of this study is the significantly improved accuracy obtained by proposed msFBCSP compared to single-session FBCSP in PD patients (median 81.3%, range 41.2-100.0% vs median 61.1%, range 25.0-100.0%, respectively; p<; 0.001). In conclusion, this study proposes a transfer learning-based multi-session based FBCSP approach which allowed to significantly improve calibration accuracy in MI BCI performed on PD patients.
    Source: EUSIPCO 2020 [Elektronski vir] : 28th European Signal Processing Conference (EUSIPCO 2020) : 24-28 August 2020, Amsterdam, the Netherlands (Str. 1353-1356)
    Type of material - conference contribution ; adult, serious
    Publish date - 2020
    Language - english
    COBISS.SI-ID - 103828227

    Link(s):

    https://www.eurasip.org/Proceedings/Eusipco/Eusipco2020/pdfs/0001353.pdf
    DOI

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