Earticle

초록

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.

목차

Abstract
 1. Introduction
 2. Noise Assisted EMD of Signal
 3. NEMD based Time Domain Filtering
 4. Experimental Results and Discussions
 5. Conclusion
 References

키워드

저자

• Most. Sheuli Akter [ Dept. of Computer Science and Engineering, University of Rajshahi, Bangladeh ]
• Md. Rabiul Islam [ Department of Computer Science and Engineering, Pabna University of Science and Technology ]
• Bimal K. Pramanik [ Dept. of Computer Science and Engineering, University of Rajshahi, Bangladeh ]
• Md. S. Islam [ Department of Computer Science and Engineering, Pabna University of Science and Technology ]
• Md. Ekramul Hamid [ Dept. of Computer Science and Engineering, University of Rajshahi, Bangladeh ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Bio-Science and Bio-Technology

• 간기

  격월간

• pISSN

  2233-7849

• 수록기간

  2009~2016

• 등재여부

  SCOPUS

• 십진분류

  KDC 505 DDC 605