A stack LSTM structure for decoding continuous force from local field potential signal of primary motor cortex (M1).

Journal: BMC bioinformatics
PMID: 33482716

Abstract

BACKGROUND: Brain Computer Interfaces (BCIs) translate the activity of the nervous system to a control signal which is interpretable for an external device. Using continuous motor BCIs, the user will be able to control a robotic arm or a disabled limb continuously. In addition to decoding the target position, accurate decoding of force amplitude is essential for designing BCI systems capable of performing fine movements like grasping. In this study, we proposed a stack Long Short-Term Memory (LSTM) neural network which was able to accurately predict the force amplitude applied by three freely moving rats using their Local Field Potential (LFP) signal.

Authors

  • Mehrdad Kashefi
    Neuroscience and Neuroengineering Research Lab., Biomedical Engineering Department, School of Electrical Engineering, Iran University of Science and Technology (IUST), Tehran, Iran.
  • Mohammad Reza Daliri
    Department of Biomedical Engineering, School of Electrical Engineering, Iran University of Science & Technology, Tehran, Iran.

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