earticle

영어

This paper focuses ocular artifacts separation of EEG signals using Noise Assisted Bi-variate adaptive based filtering. In order to facilitate clinical diagnosis and/or implement so-called brain computer interface (BCI), detecting the rhythmic activity from EEG data recorded in a noisy environment is crucial. The pre-processing of EEG signal is mandatory due to highly interference with the EEG signal. Electro-oculogram (EOG) is the most important interference that misinterpret significantly of the EEG signal for brain activity measurements. To suppress EOG data, we have used a newly developed model with empirical mode decomposition (EMD) named as noise assisted EMD (NEMD). Because the complex signals have a mutual dependence between the real and imaginary parts, so it is possible to analyses both parts simultaneously using NEMD. Here, the EEG signal and white Gaussian noise (reference signal) are combined to produce complex signal which is decomposed using NEMD to extract complex intrinsic mode functions (IMFs). Then the low frequency trend (EOG) and high frequency components (purified EEG) of recorded EEG signals are obtained partial reconstruction on the basis of the energy distribution of their intrinsic mode functions. The experimental results show that the NEMD based data adaptive filtering technique performs better.