Decoding motor intent from simulated multiple longitudinal intrafascicular electrode recordings
Abdelghani et al. BMC Neuroscience
Decoding motor intent from simulated multiple longitudinal intrafascicular electrode recordings
Mohamed Abdelghani 0
James Abbas 1
Kenneth Horch 0
Ranu Jung 0
0 Department of Biomedical Engineering, Florida International University , Miami, FL, 33174 , USA
1 School of Biological and Health Systems Engineering, Arizona State University , Tempe, AZ, 85287 , USA
From Twenty Second Annual Computational Neuroscience Meeting: CNS*2013
Paris, France. 13-18 July 2013
Signals recorded from peripheral nerves may provide an
effective and reliable means of controlling powered
prosthetic limbs. Longitudinal intrafascicular electrodes
(LIFEs) have been used to record extracellular motor
activity from peripheral nerves in upper-limb amputees
for periods up to several weeks and the ability to decode
the activity and use it for single degree-of-freedom
(DOF) control of a prosthetic arm has been
demonstrated [1]. However, simultaneous control of multiple
DOFs of the prosthesis, which is important for many
daily tasks, presents additional challenges. Recently we
developed a platform to simulate recording of
extracellular motor activity from multiple LIFE electrodes [2].
We have also designed and tested an online decoding
algorithm that utilizes these simulated recordings. Figure
1A&B shows the schematic of the decoder structure.
The decoder is composed of multiple single channel
decoders (SCDs) and a demixer. The SCD decodes
motor intent from a LIFE recording. It is composed of a
bandpass filter to attenuate noise and sharpen spikes, a
clipping function to identify spikes and a half-Gaussian
smoothing kernel to get a smoothed real-time estimate
Figure 1 (A) LIFE single channel decoder (SCD): neural signals are bandpass filtered, clipped to remove any residual background noise and
normalize spike amplitudes, and filtered to smooth the spike trains and obtain the modulating signal S (i.e. motor intent). (B) Multiple single
channel decoders followed by a demixer. (C) Actual motor intent (Red) and decoded motor intent (Blue) for an input signal with a single motor
intent. (D) Demixing of two overlapped motor intents; (Red) actual motor intents, (Blue) estimated motor intent signals after demixing.
of motor intent. The demixer identifies the motor intent
signals as corresponding to a particular motion class,
such as wrist flexion, supination etc. The demixer
requires a learning stage, where recordings from LIFEs
are correlated to motion classes. A simple batched LMS
algorithm is used to train the parameters of the
demixer. Figure 1C, display the result of a single channel
decoding of a sinusoidal motor intent. Figure 1D shows
results of demixing of two overlapped motor intents
recorded by two LIFEs: the first electrode records
activity from the two motor pools while the second electrode
records activity from only one (not shown). During the
learning stage, the demixer learns to account for
common motor intent and provide good estimates of the
two different motor intent signals. The decoder
developed here could be readily implemented in a real-time,
portable, low-power configuration to translate multiple
LIFE recordings to motor intent signals that enable
multi-DOF control of a powered prosthesis.
1. Dhillon GS , Lawrence SM , Hutchinson DT , Horch KW : Residual function in peripheral nerve stumps of amputees: implications for neural control of artificial limbs . J Hand Surg Am 2004 , 29 ( 4 ): 605 - 615 .
2. Abdelghani MN , Abbas JJ , Horch K , Jung R : A computational model to simulate neural recordings from longitudinal intrafascicular electrodes . Society for Neuroscience Conference 2012 , 584 , 20/SS19. (...truncated)