Abstract
Electroencephalogram (EEG) is a brain signal that has much information of human thought and health. For this reason, the current study on clinical brain research and brain machine interface (BMI) uses EEG signal in many applications. Due to the significant noise in EEG, signal processing to enhance signal to noise power ratio (SNR) is necessary for EEG research. The typical method is averaging many trials of ERP (event related potential) signal that represents a brain response of a particular stimulus or a task. The averaging, however, is very sensitive to timing error. In this study, we propose a time delay estimation based on simplified maximum likelihood (ML) criterion. The simulation result shows the performance of proposed scheme provides better performance than conventional schemes employing averaged signal as a reference.
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