The Processing Of EEG And The Design Of The Supply Power For Electroencephalograph

Electroencephalogram (EEG) or brain wave is a typical kind of bioelectricity signal. EEGs contain large amount of original information about the activity of nerve cells. But at the same time many kinds of artifacts are included in the raw brain signals from scalp, such as eyes blinks, electrocardiograph(ECG),electromyography (EMG) and other mechanical noises, which could degenerate the real evoked potentials. The purpose of EEG signal processing is to extract the hidden or weak patterns that probably have some physiological and/or psycho-physiological significance from EEG signals in sophisticated noise background and then analyzed their char- acteristic in different states of brain function. Aim to the limitation of traditional signal processing, the theories and methodologies based on statistical signal processing, which are used to extract the independent components from mixture signals, have been researched. The method of DFA is applied to discriminate different states of brain function. The main studies of this paper include:(1) Two important features of the chip NCP1351B are introduced. Then a power supply for electroencephalograph based on NCP1351B is designed with these two features. This power supply is more stability and consumes less power than single-end flyback switching power supply based on UC3848; Testified by the simulate experiment, this power supply with NCP1351B for electroencephalograph has strong load capacity, outstanding reliability and small noise .(2) Because of the limitation of traditional denoising method of EEG,the ICA is used to extract the independent components from mixture EEGs . This paper shows the basic principle and implementation of ICA, several main ICA algorithms and the relation of them are roundly introduced. Following the above, this paper probes into the FastICA algorithm and the extend Infomax algorithm , and gives the results of the simulate experiment. The experimental results show that these algorithms can successfully remove the artifacts such as EOG, muscle artifacts, and enhance the useful signals.(3) The DFA algorithm is introduced, scaling exponents of the different state of EEG dynamics are obtained by analyzing its fluctuation with DFA. These different states of brain function include Waken,REM (Rapid Eye Movement),sleep stage1,sleep stage2,sleep stage3,sleep stage4,closing eyes,opening eyes and making additive operation of EEGs. The scaling exponents of EEG are discriminated according to Waken and sleep EEG. The scaling exponents gradually increased from stage 1 to stage 2,3 and 4,which tells that the dynamics of brain became less activated as sleep stage goes to deep. The scaling exponents also gradually increased from making additive operation,opening eyes to closing eyes, which tells that the dynamics of brain became less activated as the scaling exponents increase.