Supervised and unsupervised learning in processing myographic patterns

Abstract

Synaptic connections in neocortex are assumed to be formed by a self-organizing process leading to emergence of the so-called self-organized maps (SOMs). Formation of SOMs is based on the unsupervised Hebbian learning rule, according to which the weight of a synaptic connection depends on the co-activation of pre- and postsynaptic neurons. On the other hand, a variety of human-machine interfaces employ supervised learning based on the correction of synaptic weights in proportion to the network error. But this learning rule has not been verified as biologically relevant. In our work, we study artificial neural networks (ANN) that classify hand gestures through electromyographic recordings (EMG). We use eight-channel electromyographic (EMG) signals acquired by a Thalmic labs Myo device as an input to a multilayer perceptron (MLP) and Kohonen’s SOM. We compare supervised (MLP) and unsupervised (SOM) learning in the task of EMG-classification. The median value of recognition fidelity (F-measure) for SOM-based recognition is F = 0.87 and for MLP classification F = 0.96. Also we reveal strong correlation between F-measures for classification EMG patterns of 37 subjects by MLP and SOM. For estimation of clustering quality of SOM we introduce two indexes: the intra-cluster index describing the cluster “compactness” and the inter-cluster index measuring the degree of cluster overlapping. There are strong correlations between F-measures for SOM classification and introduced indexes. Differences in the significance level of the correlations suggest that the classification with SOM is more negatively affected by overlapping clusters than their large size.