Applying EMD Method to Remove EEG of Eye Blink Artifacts in Measuring Fatigue State

Electroencephalogram (EEG) is important in examine brain activities. Among the many sources of artifacts in EEG recording, eye activity plays a dominate role. There are many methods developed to remove it, but most of them have their shortcomings or restrictions. The most frequently used method to remove ocular artifacts is independent component analysis (ICA), but it can be applied in multi channel EEG data only. A new method was presented in this thesis to remove eye blink artifacts from EEG data which is based on the mathematical method called empirical mode decomposition (EMD). The method has several advantages that make it more convenient and flexible compared with other methods. It could be used in single channel EEG data and needn’t modify personal parameters to fit each data. When applying the method, use EMD to decompose EEG data into sever intrinsic mode functions (IMF) and one residue first, and then remove artifacts liked data by retaining IMF from first to fifth only and eliminating over limit vibration of these IMF. The thesis also utilizes this EMD based eye blink signal removing method in an experiment which designed to estimate fatigue with EEG. This method works effectively in the experiment in removing artifacts, not only eye blink artifacts but also other kinds of artifacts. The design of this experiment needs 25 subjects doing a planed work, and then measuring subjective fatigue transfer and EEG pattern transfer between non-worked and worked states. A fatigue scale was used in estimating fatigue levels. A brain wave machine was used to measure EEG pattern. After getting the data, this research tries to find connections between EEG and fatigue with correlation of statistics.