RESEARCH AREA II: BRAIN MACHINE INTERFACE
NSF funded project
The parallel recordings from large neuron populations in the sensory cortex and primary motor cortex reveal the rich information encoded into neural signals, and guide research in restoring cognitive and motor behaviors. In such devices, the quality of information relies on the density and resolution of neural signals being measured (1). The current state-of-the-art brain-machine interface (BMI) is capable of hundreds of simultaneous electrocorticography (ECoG) recordings and was successfully used for alleviating gait deficits in primates with spinal cord injury (2–4). However, the throughput remains insufficient to be clinically relevant and significant improvements in the recording throughput are required for BMIs to help severely disabled patients to fully regain mobility or other impaired functions (5). Restoring limb movements may require a BMI to monitor 5,000 – 10,000 neurons simultaneously (6), whereas producing full-body movements may require 100,000 neural measurements (5). In recent years, many researchers are leading the BMI development to increase the throughput beyond 1000-ch recordings (2, 3, 7–10). The goal of our work is to develop a new modality that can interface with neurons.