Description:
Many
companies sell neurophysiology data acquisition systems that are bulky, wired,
and very expensive, requiring large computational power via PCs to sort neural
spikes. In addition, the spike-sorting task is a fundamental step in the design
of clinically viable brain machine interfaces for restoring normal sensory and
motor function of disabled people. Many of these systems require high electrode
channel count and large number of cells to operate efficiently and reliably.
Description
Michigan
State University’s technology is an algorithm for sorting neural spikes fired by
multiple nerve cells and simultaneously recording them using a single or an
arrow of microelectrodes implanted adjacent to the cell population. The
algorithm is hardware optimized to be readily implemented in ultra-low power and
miniaturized electronic circuits that are feasible to include in a fully
implanted system.
The
technology classifies multiple spike waveforms (spike sorting) to permit
extracting spike trains of individual neurons from the recorded mixture of
signals and reduces the ultra-high communication bandwidth needed to transmit
the recorded raw data to an external PC. It also extracts information reliably
from single cell activity to characterize brain function in daily life and in
subjects suffering from many neurological diseases and disorders, such as
Parkinson’s disease and epilepsy.
Benefits
·
Decompression not necessary:
Sorts the spikes within the compressed representation of the raw data without
the need to decompress and sort in the traditional sense.
·
Suited for large-scale
interfacing: Well suited for large-scale interfacing with the
nervous system where hundreds (or potentially thousands) of cells would need to
be monitored simultaneously to infer useful information about neural
activity.
·
Real-time operation: Operates in
real time, making it suitable for closed loop brain machine interface system
design in clinical applications.
·
Preserves biological information:
Preserves all the biological information in the recorded data.
·
Adaptive and flexible: Adaptive
to changes in spike waveform shapes typically observed during long-term
experiments/clinical use.
·
Suitable for implantable systems:
The technology is hardware friendly, making it suitable for large-scale
integration in implantable microsystems.
Applications
·
Neurotechnology
·
Assistive technology for people with severe paralysis or
spinal cord injury or prosthetic limbs
·
Brain machine interfaces for restoring normal sensory and
motor function of disabled people
IP Protection
Status
Patent
pending
