A Novel Approach of Decoding Facial Nerve Signals for Predicting Rabbit Whisker Movements

This work proposed a decoding algorithm of the bioelectrical signal acquired from a head-fixed rabbit subject's buccal nerves related to the corresponding whisker movements. The nerve-whisker matching algorithm is fulfilled by the signal pre-processing and the neural networks consisting of a Long Short-Term Memory Recurrent Neural Network (LSTM) layer and a fully-connected layer. Concordance Correlation Coefficient Loss is applied for training to capture the intricate relationships between nerve signals and corresponding whisker movements. The algorithm is also able to decode both amplitude and phase information of the recorded signal. A decoding accuracy of 80% is achieved according to in-vivo experimental results.