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- W4294975456 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%." @default.
- W4294975456 created "2022-09-08" @default.
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- W4294975456 date "2022-07-11" @default.
- W4294975456 modified "2023-10-16" @default.
- W4294975456 title "Improved Grip Force Prediction Using a Loss Function that Penalizes Reward Related Neural Information" @default.
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- W4294975456 doi "https://doi.org/10.1109/embc48229.2022.9871920" @default.
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