From biomedical signal processing techniques to fmri parcellation


AL-Jobouri H. K. , Cankaya İ. , Karal Ö.

Biosciences Biotechnology Research Asia, vol.12, no.2, pp.1115-1138, 2015 (Refereed Journals of Other Institutions) identifier identifier

  • Publication Type: Article / Article
  • Volume: 12 Issue: 2
  • Publication Date: 2015
  • Doi Number: 10.13005/bbra/1764
  • Title of Journal : Biosciences Biotechnology Research Asia
  • Page Numbers: pp.1115-1138
  • Keywords: Adaptive Filters, Cross-Correlation and Coherence, Digital Filters, Ensemble Averages, fMRI, Multivariate Analyses, Optimal Filter, Parcellation., Principal Component Analysis,Independent Component Analysis, Time-Frequency Analysis, Wavelet Analyses

Abstract

In this paper a comparison between numbers of digital signal processing techniques are introduced, and especially its applications with biosignals like ECG, EEG and EMG. These techniques are used for data extraction from biosignals, processing and analyzing the main characteristics, advantages and disadvantages of these techniques are also introduced. Multivariate analysis is one of the most important techniques that has wide applications in biomedical fields and can be applied for different medical signals and images. For example, this technique is commonly used for the analysis of functional Magnetic Resonance Imaging (fMRI) which can be applied to identify technical and physiological artifacts in fMRI. Second part of this paper introduces a short survey on fMRI parcellation technique and especially based on a data driven approach. Brain parcellations divide the brain's spatial domain into a set of non-overlapping regions or modules,and these parcellations are often derived from specific data driven or clustering algorithms applied to brain images. This paper considers as the first paper that presented a survey on using different DSP techniques with a variety of biosignal and analyzed these biomedical signals as well as introduced one of the most important application of multivariate methods with fMRI.