EEG Signal Classification: Introduction to the Problem

Abstract

The contribution describes the design, optimization and verificationof the off-line single-trial movement classification system. Four typesof movements are used for the classification: the right index fingerextension vs. flexion as well as the right shoulder (proximal) vs.right index finger (distal) movement. The classification systemutilizes hidden information stored in the characteristic shapes ofhuman brain activity (EEG signal). The great variability of EEGpotentials requires using of context information and hence theclassifier based on Hidden Markov Models (HMM). The suitableparameterization, model structure as well as training andclassification process are suggested on the base of spectral analysisresults and experience with the speech recognition. The training andthe classification are performed with the disjoint sets of EEGrealizations. Classification experiments are performed with 10 randomlychosen sets of EEG realizations. The final average score of thedistal/proximal movement classification is 80%; the standard deviationof classification results is 9%. The classification of the extension /flexion gives comparable results.