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  1. AU="Heydari Beni, Nargess"
  2. AU="Pinter, Emily N"
  3. AU="Hogan, William J"
  4. AU="Tikute, Sanjaykumar"
  5. AU="Lu Shi"
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  1. Article ; Online: Force decoding using local field potentials in primary motor cortex: PLS or Kalman filter regression?

    Heydari Beni, Nargess / Foodeh, Reza / Shalchyan, Vahid / Daliri, Mohammad Reza

    Australasian physical & engineering sciences in medicine

    2020  

    Abstract: The development of brain-computer interface (BCI) systems is an important approach in brain studies. Control of communication devices and prostheses in real-world scenarios requires complex movement parameters. Decoding a variety of neural signals ... ...

    Abstract The development of brain-computer interface (BCI) systems is an important approach in brain studies. Control of communication devices and prostheses in real-world scenarios requires complex movement parameters. Decoding a variety of neural signals captured by micro-wire arrays is a potential applicant for extracting movement-related information. The present work was conducted to compare the functionality of partial least square (PLS) regression and Kalman filter to predict the force parameter from local field potential (LFP) signals of the primary motor cortex (M1). The signals were recorded using a 16-channel micro-wire array from the forelimb-related area of the M1 of three rats performing a behavioral task in which the force signal of the rat's forelimb paw was generated. Our results show that PLS regression and Kalman filters with the mean performance of 0.75 and 0.72 in terms of the correlation coefficient (CC) and 0.37 and 0.48 in terms of normalized mean square error (NMSE), respectively, are effective methods for decoding the force parameter from LFPs. Kalman filter underperforms PLS both in performance and speed. Although adding nonlinearity to the Kalman filter results in equally accurate CC performance as PLS, it has even more computational cost. Therefore, it is inferred that nonlinear methods do not necessarily have better functionality than linear ones and PLS, as a simple fast linear method could be an effectively applicable regression technique for BCIs.
    Language English
    Publishing date 2020-01-02
    Publishing country Netherlands
    Document type Journal Article
    ZDB-ID 46226-3
    ISSN 1879-5447 ; 0158-9938
    ISSN (online) 1879-5447
    ISSN 0158-9938
    DOI 10.1007/s13246-019-00833-7
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  2. Article ; Online: Continuous Force Decoding from Local Field Potentials of the Primary Motor Cortex in Freely Moving Rats.

    Khorasani, Abed / Heydari Beni, Nargess / Shalchyan, Vahid / Daliri, Mohammad Reza

    Scientific reports

    2016  Volume 6, Page(s) 35238

    Abstract: Local field potential (LFP) signals recorded by intracortical microelectrodes implanted in primary motor cortex can be used as a high informative input for decoding of motor functions. Recent studies show that different kinematic parameters such as ... ...

    Abstract Local field potential (LFP) signals recorded by intracortical microelectrodes implanted in primary motor cortex can be used as a high informative input for decoding of motor functions. Recent studies show that different kinematic parameters such as position and velocity can be inferred from multiple LFP signals as precisely as spiking activities, however, continuous decoding of the force magnitude from the LFP signals in freely moving animals has remained an open problem. Here, we trained three rats to press a force sensor for getting a drop of water as a reward. A 16-channel micro-wire array was implanted in the primary motor cortex of each trained rat, and obtained LFP signals were used for decoding of the continuous values recorded by the force sensor. Average coefficient of correlation and the coefficient of determination between decoded and actual force signals were r = 0.66 and R
    Language English
    Publishing date 2016-10-21
    Publishing country England
    Document type Journal Article
    ZDB-ID 2615211-3
    ISSN 2045-2322 ; 2045-2322
    ISSN (online) 2045-2322
    ISSN 2045-2322
    DOI 10.1038/srep35238
    Database MEDical Literature Analysis and Retrieval System OnLINE

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