Learning of EMG-patterns by adaptive logic networks

An adaptive logic network (ALN) has been used for nonparametric identification of the system consisting of two peripheral nerves and two muscles in the freely moving cat. The aim of this identification was to design a rule-based control for a functional electrical stimulation (FES) of the cat's hind limb. We recorded from peripheral nerves and muscles while a chronic cat walked an a powered treadmill. We noticed a very reproducible firing in tibial and superficial peroneal nerves related to patterns of EMG activity in medial gastrocnemius (MC) and anterior tibialis (AT) muscles in the cat's hind limb with respect to a phase of the gait cycle (e.g. beginning stance, beginning swing). We succeeded to restore and predict EMG activity of MG and AT muscles from neural recordings after applying Adaptive Logic Networks (ALNs). ALN is a type of artificial neural network having a tree configuration and using a Boolean operations in its nodes. We supplied a training set consisting of coded recordings from nerves and muscles (20 cat's steps were sampled at 50 Hz) for supervised training. Applied encoding and training parameters resulted in a reasonably short training time and high correlation between an originai and predicted EMG signals. The variance accounted for (VAF) by the ALN prediction of the test data was 80% qualify- ing this technique as a good candidate for implementation in real-time FES control systems.