Improved Grip Force Prediction Using a Loss Function that Penalizes Reward Related Neural Information

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%.