Research On Feature Extraction And Analysis Of EEG Signals

As the system conducting life activities of human body, neural system plays a fairly significant role in actuating behaviors, perceiving the environment and reflecting the health status. Through detecting the neural system, we are able to acquire lots of information about human body's mechanics as well as inner activities which could rarely be obtained by subjective description or other detecting methods. In the fields of neuroscience and neuroinformatics, ElectroEncephaloGram(EEG), which sources from the brain itself, is now being paid more and more attention to from researchers due to its various advantages such as the convenience in operation, high temporal resolution, low cost and non-invasive operation. Furthermore, after gaining the valid data, it is quite necessary to extract features which we are interested in and completed the classification works, these are all indispensable and extremely important in EEG data analysis. On the one hand, based on Hilbert-Huang Transform, this paper just analyzes a set of sleep EEG data from our sleep experiments in light of pattern matching to evaluate the effect of a sort of health mattress, where the processes of feature extraction and classification for EEG data are simutannesously expatriated. It is a creative attemption to apply EEG into engineering fields. Meanwhile, we also proposed a novel method to estimate sleep status with the support of the sleep dataset which improved some drawbacks in the traditional method. On the other hand, this paper introduces a new kind of high definition EEG electrode array which may acquired EEG date with high spatial resolution, and therefore, throughout some specific frame detection approaches and BP neural network, we are able to discriminate different mechanical stimuli on different fingers of right hand successfully. This research combined the advantages of EEG and ECoG and would gift us a useful tool for future exploration in neuroinformatics.