The Research De-noising Method Based On Kalman Filter For EEG

A brain computer interface (BCI) is a new man-machine interactive communicationsystem which can realize communication between people and external environment. Itdoes not pass through the human body inherent nerves and muscles, only need to usebrain consciousness to achieve information exchange with the outside world. Featureextraction, pattern classification of EEG is a hot issue in the study of brain-computerinterface, but EEG de-noising preprocessing is an essential part of the research. In thispaper, we use the kalman filtering method for EEG de-noising.First of all, a method of using kalman filter to eliminate power line interference isresearched to solve the problems of power line interference which is included in the EEG.Power line interference in the text will be dealt with as a sinusoidal signal. Kalman filtermodel is established through variation relationship of the sinusoidal signal, and get therequired parameters which is need in the course of its operation process. We deal with theconversion of frequency、phase and amplitude sinusoidal signal on the experiment, andestimate value of each sub-frequency in sine superimposed signal. And then experimentsabout standard EEG and actual EEG were carried out. Experimental results show that thismethod is good to wipe off power line interference.Secondly, for the issues that EEG is affected by a variety of interference such aseyeblink、cardiac electric activity and myoelectric activity etc, we achieved a method ofkalman filter modeling and model parameter estimation. The open brain ComputerInterface Competition experimental data (BCI Competition III dataset I) is used in theexperiment. The experimental results show that after the de-noising pretreatment of thismethod, Classification accuracy has been greatly improved and is better than thepretreatment method of wavelet de-noising and spectral subtraction etc.Finally, the extended kalman filtering method is discussed. We use a sine signal inthe simulation experiment. We get sine signal and its observation through the nonlinearcalculated, and carry through extended kalman filter to the observations. Experimentalresults show that this method is good to restore the original signal.