Brain-machine interfaces

A graph showing that neural recordings measured at different spatial scales differ in their performance and reliability.
Figure 1 – Neural recordings measured at different spatial scales differ in their performance and reliability.

A brain-machine interface (BMI) records neural activity from the brain and processes it to generate a control signal. Neural activity recorded in the motor system, for example, can be used to move a prosthetic device such as a computer interface, wheelchair or a robotic arm. The ideal brain-machine interface should be reliable and support high-performance control. However, neural signals can be measured in many different ways (see Sidebar). Neural signals differ in the performance and reliability (see Figure 1).  Spiking activity can support high-performance control but is difficult to obtain reliably. In contrast, neural activity measured at larger scales (LFP/ECoG) offers less detailed control than spiking, but may be more reliable. We are interested in understanding how to optimize BMI performance and reliability by incorporating neural signals at different spatial scales. We have been comparing the information contained in different neural signals. We have also recently engineered a new device which allows us to record spiking, LFP and ECoG signals at the same time from the same group of neurons.

 

Another direction in BMI is inspired by the success of Deep-Brain Stimulation. Stimulation-based BMIs process neural activity to control patterned brain stimulation. These systems close-the-loop between stimulation and recording to correct abnormal brain activity patterns. This approach has potential to treat a wide-range of neurological disorders (see Brain Stimulation).

Lab Researchers

Amy Orsborn, Kyle Brubaker, David Markowitz, David Putrino, Joshua Seideman, Adam Weiss, Yan Wong

Collaborators

John Rogers, Jonathan Viventi, Iahn Cajigas, Maryam Shanechi

Papers

Modeling multiscale causal interactions between spiking and field potential signals during behavior

Wang et al. (2022) Journal of Neural Engineering

 

Flexible, high-resolution thin-film electrodes for human and animal neural research

Chiang et al. (2021) Journal of Neural Engineering

 

Development of a neural interface for high-definition, long-term recording in rodents and nonhuman primates

Chiang et al. (2020) Science Translational Medicine

 

Parsing learning in networks using brain–machine interfaces

Osborn, Pesaran (2017) ClinicalKey

 

Utilizing movement synergies to improve decoding performance for a brain machine interface

Wong et al. (2013) Conf Proc IEEE Eng Med Biol Soc

 

Optimizing the decoding of movement goals from local field potentials in Macaque cortex

Markowitz et al. (2011) J Neurosci

 

Selecting the signals for a brain-machine interface

Andersen et al. (2004) Curr Opin Neurobiol


Project Funded By
DARPA Biological Technologies Office