Comparison of motion field of EEG topo-maps for tracking brain activation

Human Brain can be divided into four different lobes: frontal lobe, parietal lobe, temporal lobe, and occipital lobe. Every lobe has its specific function. Electroencephalography (EEG) measures the electrical activity of human brain that is generated by synchronized activity of thousands of neurons. In this paper, we have developed an algorithm to track brain activity by using motion field generated by the Horn-Schunck (HS) optical flow algorithm. We have acquired EEG data from 20 subjects using oddball experiment to investigate the flow pattern of EEG signal (activity) across brain lobes during a specific activity. The EEG data is converted into EEG topographic maps (topo-map). The flow of EEG activity between consecutive topo-maps is estimated by using flow of brightness pattern in an image. A tracking algorithm is developed by tracking the motion vectors to examine the flow of EEG signal that depends on the overlapping of the motion field between current frame and next frames. The motion field is generated and the result of tracking is compared with full search (FS) block matching motion estimation algorithm. It has been observed that the motion field produced by HS method gives better results than the FS algorithm.