A comparative study of distance/dissimilarity measures used for classification of electroencephalogram signals

Biological signal is a general term that refers to the signal measured from a biological system. Some representative examples include the electrocardiogram, the blood pressure waveform, the cellular action potential, etc. Many biological signals show distinctive waveform morphology which reflects the dynamics of the biological systems. The electroencephalogram (EEG) signal is a measure of the summed activity of approximately 1–100 million neurons lying in the vicinity of the recording electrode, and may provide insight into the functional structure and dynamics of the brain. Therefore, the exploration of hidden dynamical structures within EEG signals is of both basic and clinical interests. In clinical practices EEG is used to diagnose or monitor the following health conditions. In a computer aided diagnosis abnormal activities are detected and normal and abnormal activities are distinguished. An electroencephalograph (EEG)-based communication system, also known as brain–computer interface (BCI), utilizes the information in EEG and provide a new communication channel for patients with several motor disabilities, such as brain stem infarct or amyotrophic lateral sclerosis. The BCI requires classification or distinction of the information in EEG or state of EEG. All these applications necessitate distinction of state of the EEG. The distance or dissimilarity measures separability of the states of the EEG and provides that two or more EEG patters are different and each of them corresponds to a distinct state. In this study distance/dissimilarity measures for classifying EEG will be investigated and distinct features they recognize will be determined