Hidden pattern discovery on event related potential EEG signals.
EEG signals are important to capture brain disorders. They are useful for analyzing the cognitive activity of the brain and diagnosing types of seizure and potential mental health problems. The Event Related Potential can be measured through the EEG signal. However, it is always difficult to interpret due to its low amplitude and sensitivity to changes of the mental activity. In this paper, we propose a novel approach to incrementally detect the pattern of this kind of EEG signal. This approach successfully summarizes the whole stream of the EEG signal by finding the correlations across the electrodes and discriminates the signals corresponding to various tasks into different patterns. It is also able to detect the transition period between different EEG signals and identify the electrodes which contribute the most to these signals. The experimental results show that the proposed method allows the significant meaning of the EEG signal to be obtained from the extracted pattern.
Ng
Kam Swee
KS
Department of Computer Science, Chonnam National University, Gwangju 500-757, South Korea. kamswee@gmail.com
Yang
Hyung-Jeong
HJ
Kim
Sun-Hee
SH
D000465:Algorithms; D000818:Animals; D001921:Brain / Q000033:anatomy & histology / Q000502:physiology; D001931:Brain Mapping; D003198:Computer Simulation; D004569:Electroencephalography / Q000379:methods*; D005071:Evoked Potentials / Q000502:physiology*; D017143:Fuzzy Logic; D006801:Humans; D008959:Models, Neurological; D009415:Nerve Net; D012815:Signal Processing, Computer-Assisted; D013647:Task Performance and Analysis; D014584:User-Computer Interface