TY  - JOUR
PY  - 2022//
TI  - Improved grip force prediction using a loss function that penalizes reward related neural information
JO  - Annual International Conference of the IEEE Engineering in Medicine and Biology Society.
A1  - Kumar, Jaganth Nivas Asok
A1  - Francis, Joseph Thachil
SP  - 2336
EP  - 2339
VL  - 2022
IS  - 
N2  - Neural activity in the sensorimotor cortices has been previously shown to correlate with kinematics, kinetics, and non-sensorimotor variables, such as reward. In this work, we compare the grip force offline Brain Machine Interface (BMI) prediction performance, of a simple artificial neural network (ANN), under two loss functions: the standard mean squared error (MSE) and a modified reward penalized mean squared error (RP_MSE), which penalizes for correlation between reward and grip force. Our results show that the ANN performs significantly better under the RP_MSE loss function in three brain regions: dorsal premotor cortex (PMd), primary motor cortex (M1) and the primary somatosensory cortex (S1) by approximately 6%.<p />  <p>Language: en</p>
LA  - en
SN  - 2375-7477
UR  - http://dx.doi.org/10.1109/EMBC48229.2022.9871920
ID  - ref1
ER  -