Population decoding of motor cortical activity using a
generalized linear model with hidden states
Vernon Lawhern, Wei Wu, Nicholas G. Hatsopoulos, and Liam
Paninski
In press, J. Neurosci. Meth.
Generalized linear models (GLMs) have been developed for modeling and
decod- ing population neuronal spiking activity in the motor
cortex. These models provide reasonable characterizations between
neural activity and motor behavior. However, they lack a description
of movement-related terms which are not observed directly in these
experiments, such as muscular activation, the subject's level of
attention, and other internal or external states. Here we propose to
include a multi-dimensional hidden state to address these states in a
GLM framework where the spike count at each time is described as a
function of the hand state (position, velocity, and acceleration),
truncated spike history, and the hidden state. The model can be
identified by an Expectation-Maximization algorithm. We tested this
new method in two datasets where spikes were simultaneously recorded
using a multi-electrode array in the primary motor cortex of two
monkeys. It was found that this method significantly improves the
model-fitting over the classical GLM, for hidden dimensions varying
from 1 to 4. This method also provides more accurate decoding of hand
state (lowering the Mean Square Error by up to 29% in some cases),
while retaining real-time computational efficiency. These improvements
on representation and decoding over the classical GLM model suggest
that this new approach could contribute as a useful tool to motor
cortical decoding and prosthetic applications.
Preprint | Liam
Paninski's research page