@article{ref1,
title="Improved grip force prediction using a loss function that penalizes reward related neural information",
journal="Annual International Conference of the IEEE Engineering in Medicine and Biology Society.",
year="2022",
author="Kumar, Jaganth Nivas Asok and Francis, Joseph Thachil",
volume="2022",
number="",
pages="2336-2339",
abstract="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>",
language="en",
issn="2375-7477",
doi="10.1109/EMBC48229.2022.9871920",
url="http://dx.doi.org/10.1109/EMBC48229.2022.9871920"
}