Neural decoding of hand motion using a linear state-space model
with hidden states
Wei Wu, Jayant E. Kulkarni, Nicholas G. Hatsopoulos, and Liam
Paninski
IEEE Transactions on Neural Systems and Rehabilitation
Engineering 17: 370-378
The Kalman filter has been proposed as a model to decode neural
activity measured from the motor cortex in order to obtain real-time
estimates of hand motion in behavioral neurophysiological
experiments. However, currently used linear state-space models
underlying the Kalman filter do not take into account other behavioral
states such as muscular activity or the subject's level of attention,
which are often unobservable during experiments but may play important
roles in characterizing neural controlled hand movement. To address
this issue, we depict these unknown states as one multi-dimensional
hidden state in the linear state-space framework. This new model
assumes that the observed neural firing rate is directly related to
this hidden state. The dynamics of the hand state are also allowed to
impact the dynamics of the hidden state, and vice versa. The
parameters in the model can be identified by a conventional
Expectation- Maximization algorithm. Since this model still uses the
linear Gaussian framework, hand-state decoding can be performed by the
efficient Kalman filter algorithm. Experimental results show that this
new model provides a more appropriate representation of the neural
data and generates more accurate decoding. Furthermore, we have used
recently developed computationally efficient methods by incorporating
a priori information of the targets of the reaching movement. Our
results show that the hidden-state model with target-conditioning
further improves decoding accuracy.
Preprint | Liam
Paninski's research page