Andrew Meng
Research Mentor(s): Matt Mender
Mentor Department: Neurosurgery
Authors:
Session: Session 5 (2:00pm – 2:50pm)
Presentation Type: Poster 33
Abstract
In the rapidly developing field of intracortical brain machine interfaces (iBCIs), the goal is to analyze neural data and decode the naturalistic movements they represent in order to replicate them in prosthetic limbs via the interpreted signals. Consequently, it is imperative to have efficient methods of analyzing these data and formatting them in a way which allows the convenient manipulation of collected neural activity. To address this, a data processing pipeline was constructed to process Intan Technology’s .rhd file format, proprietary to their commonly used and low-cost electrophysiology acquisition platform, and convert it to a hexadecimal .BIN format in preparation for Kilosort 4’s spike-sorting Python library. This is a key development as data pipelining for TDT electrophysiology recording software has already been produced, but the system is very expensive, limiting its availability. This pipeline yields more compatibility with state of the art spike-sorting softwares such as Kilosort 4, paving the way for widely-accessible and high throughput methods of interpreting of neural data and understanding the mechanisms of the neural circuitry underlying the examined movements. Looking forward, now that an effective method of processing neural data inputs has been developed, the next step is to optimize the analysis of the data. Particularly, now that spiking events can be detected and identified, neural networks will be implemented with the aim of decoding non-prehensile movements.
