Feature Set Extraction Algorithm based on Soft Computing Techniques and Its Application to EMG Pattern Classification

Abstract

Recognizing bio-signals, such as EMG, EEG, EOG and ECG, is a promising theme of study since it provides with a convenient means for human-machine interaction. Various approaches of determining features of bio-signals were known for discerning predefined motions/intentions of human, but most of them are applicable mostly only to a single subject, due to inherent characteristics of bio-signals. Lately, several new types of pattern classifier with known features have been proposed to cope with the problem of subject-dependency, but their error rates are still conspicuous when accommodating multiple subjects. Based on the soft computing techniques, this paper presents a comparative experimental study to minimize the subject-dependency. It is shown that the induced feature vector set obtained by the proposed algorithm has less subject-dependency than other existing methods.