Force Decoding from Motor Cortex Neurons for BCI Applications

Brain-computer interface (BCI) system has been introduced to extract motor commands with the goal of movement restoration after spinal cord injury (SCI). In this project, we tried to present new BCI techniques with potential for near-term use in human subjects for movement restoration.

We used local field potential (LFP) signals that provides more stable movement-relateda information and requires less power for hardware implementation. Furthermore, we focused on decoding of force information from LFPs because of its key role to control the end-point of prosthetic arms or direct stimulation of muscles and spinal cord for movement restoration. In the online phase, we used force signal decoded from the 16 channel LFPs to control a mechanical arm in real-time. In the next step of project, we decoded motor command from the multichannel LFPs after SCI to continuously control the amplitude of epidural stimulation delivered to the spinal cord below the injury. This system demonstrates that LFP-controlled epidural spinal stimulation can reanimate volitional forelimb functional movement in 3 rats with spinal cord injury. 

  • Nargess Heydari
  • Reza Foodeh
  • Soshi Samejima
  • Adrien Boissenin
  • Chet Moritz
  • Vahid Shalchyan
  • Mohammad Reza Daliri