A novel classification method with unlearned-class detection based on a gaussian mixture model

This paper proposes a novel method of estimating posteriori probability for learned and unlearned classes based on a Gaussian mixture model (GMM). With prior distributions of learned and unlearned classes defined as a novel GMM incorporating a one-versus-the-rest classifier, any defined/undefined class can be classified through training of the classifier using given training samples. This method can be used for bioelectric signal discrimination in various applications such as human-machine interfaces and diagnosis support systems. In the experiments reported here, artificial data generated from Gaussian distributions and electromyogram (EMG) patterns measured from the forearm muscles of a volunteer were classified to demonstrate the capabilities of the proposed method for learned and unlearned class discrimination. The results showed that the approach produces high performance for classification of learned (artificial data: 100%; EMG patterns: 95.6%) and unlearned (artificial data: 93.4%; EMG patterns: 70.4%) classes based on simple neural network comparison, and indicated that the proposed method is applicable to human-machine interfaces such as prosthetic hand control systems.