A data mining application on cognitive EEG recording

Development of computer and data-storage technology caused new techniques to arise to get these data useful in daily life. Especially complex statistical methods became easily usable on large amounts of data. This new approach (named as Knowledge Discovery in Databases or Data Mining) came with many advantages for every domain. It provided the transition from data to knowledge.Human body is a complex system with subsystems in itself generating many data in various types. Brain is individually one of the vital parts of human body. It has complex communication mechanisms and many unexplored regions and functions. Electroencephalography (EEG) is a method which is used to present the electrical activity of the brain. In EEG technique, electrodes located on head receives small voltage changes produced by brain over time during a process or even in asleep. These data are used for many areas in especially epilepsy, sleep disorders, biophysics, neuroscience, etc.This thesis aims applying some of the data mining methods on EEG data recorded during dichotic listening test. EEG data were examined in detail, analysed, partitioned and labelled. Statistical similarity measure ZM statistic was used as a tool for comparing the similarity or dissimilarity of signals received from different electrodes for different dichotic stimuli.ZM statistic is a powerful tool in identifying similarity of signals in amplitude but not in shape. To avoid this deficiency data were transformed into difference signals to detect the behavioural similarity. Applying ZM to this transformed signals gave more reliable results in signal similarity. Some of the similarities which were not found before transformation arose in the transformed signals similarity. By this adjustment of data, signals moving together in different amplitudes were also detected.Besides, a clustering was performed on electrodes using dendrogram visualization to support the similarity results.