Michael A. Schwemmer and Colleagues to Build Brain-Computer Interface

The latest research in neuro-technology involves producing brain computer interfaces, also called BCIs, which facilitate interaction between human/animal brains and computer systems. And a pioneering research is being conducted by Michael A. Schwemmer and several of his colleagues on this subject.

More Details about BCI Technology Associated Research
This technology is expected to be highly useful in case of treating spinal cord injuries and or even paralysis. In such situations, patients may be able to use neural decoders that can access part of their brain to operate a prosthetic limb or even imitate the action of a properly working limb through functional electrical stimulation, also called FES.

The study was covered in a recently published Nature Medicine article, and details the research done by Michael A. Schwemmer and colleagues on the neural network decoders. These devices where used on a participant with tetraplegia formed due to a spinal cord injury. Their research focuses on addressing several key needs identified by end-users of BCI systems such as high accuracy, minimal daily setup, rapid response time, and multi-functionality. All these characteristics are heavily influenced by a BCI’s particular neural decoding algorithm.

This group describes numerous different approaches to training and testing three variations on neural network decoders in comparison with each other. These comparisons are made along with the support by benchmark support vector machine decoder. The four BCI decoder paradigms were developed and tested over the course of several years in association with the participant having tetraplegia. The participant had a 96-channel microelectrode array implanted in the area of his left primary motor cortex corresponding to the hand and arm portions. Using intracortical data collected from 80 sessions over 865 days, the investigators trained and evaluated these BCI decoders. These sessions consisted of two 104-second blocks of a four-movement task. This task detailed actions such as index extension, index flexion, wrist extension, and wrist flexion.

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