Force decoding using local field potentials in primary motor cortex: PLS or Kalman filter regression?
| Autor: | Reza Foodeh, Vahid Shalchyan, Nargess Heydari Beni, Mohammad Reza Daliri |
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| Rok vydání: | 2019 |
| Předmět: |
Radiological and Ultrasound Technology
Correlation coefficient Computer science business.industry Stationary wavelet transform Biomedical Engineering Biophysics Pattern recognition Kalman filter Signal Regression 030218 nuclear medicine & medical imaging 03 medical and health sciences Nonlinear system 0302 clinical medicine 030220 oncology & carcinogenesis Radiology Nuclear Medicine and imaging Artificial intelligence business Instrumentation Decoding methods Biotechnology Brain–computer interface |
| Zdroj: | Australasian physicalengineering sciences in medicine. |
| ISSN: | 1879-5447 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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