Neural Ensemble Decoding of Rat´s Motor Cortex by Kalman Filter and Optimal Linear Estimation

In this paper, rats were trained to press a lever over a threshold to get water as rewards, and neural ensemble activities in primary motor cortex (MI) and pressure signal of the lever were recorded synchronously. Meanwhile, two algorithms, Kalman filter (KF) and Optimal Linear Estimation (OLE), were used to decode neural ensemble activities around the pressing events. After training, the pressure values were extracted from the neural signals by those decoders, and the performances were compared by Correlation Coefficient (CC) and Mean Square Error (MSE) with pressure values recorded from the lever. KF was better than OLE when the bin size < 100 ms with CC > 0.7, but it was not applicable in this experiment when the bin size > 100 ms for excessively smoothed. And OLE performs better when bin size > 100 ms for diminishing the randomicity of neural firing, which might imply that KF is more applicable in real-time system.