Automatic discrimination of myoelectric signals via parallel cascade identification

It has recently been shown that it is possible to discriminate accurately among myoelectric signals underlying different muscle contraction types, specifically elbow flexion and extension and forearm pronation and supination. It was reported that once a number of distinctive features had been extracted from the myoelectric signals, a neural network could be trained to distinguish the contraction types with an impressively high accuracy. In the present paper, we show that a technique known as parallel cascade identification can be used to construct classifiers that can also accurately differentiate the contraction types. The use of parallel cascades has the benefit of dispensing with the need for feature extraction, so that raw myoelectric signal data can be used directly. In addition, very little data are required to train the parallel cascades to distinguish accurately novel incoming myoelectric signals. Results of using parallel cascades to distinguish forearm pronation, supination, and elbow flexion are presented.