Real-Time Decision Fusion for Multimodal Neural Prosthetic Devices
Flowchart describing fusion of Kalman filter (KF), PVA, and the optimal linear decodes using the Kalman filter and ANNs. Experimental trials contained three major phases: (i) individual decoder training, (ii) fusion decoder training, and (iii) final testing. In each experiment, individual decoders were first trained using the same simulated spike count data. Next, fusion decoders were trained on the individual decoders' outputs (predicted velocity components in x and y dimensions) for a separate fusion training dataset. An additional validation dataset was employed to prevent overtraining of ANNs. In final testing, trained individual decoders were used to predict the 2-d velocities, which were then compiled as input for fusion decoders. Endpoint velocity predictions from all decoders were then compared for accuracy. See Methods for details of the evaluation methodology.