From Biomedical Signal Processing Techniques to fMRI Parcellation

Hadeel Kassim AL-Jobouri^{1, 2}, İlyas Çankaya³, Omer Karal⁴

¹Medical Engineering Department, College of Engineering, Al-Nahrain University, Baghdad, Iraq. ²Ph.D. Student at Yildrim Beyazit University, Institute of Science and Technology, Ankara, Turkey. ³Associate Professor Dr.at Yildrim Beyazit University, Institute of Science and Technology, Ankara, Turkey. ⁴Assist. Prof. Dr. at Yildrim Beyazit University, Institute of Science and Technology, Ankara, Turkey.  

ABSTRACT: In this papera 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.  

KEYWORDS: Digital Filters; Cross-Correlation; Coherence; Ensemble Averages; Time–Frequency Analysis; Wavelet Analyses; Optimal Filter; Adaptive Filters; Multivariate Analyses; Principal Component Analysis; Independent Component Analysis; fMRI; Parcellation

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