International Journal For Multidisciplinary Research

E-ISSN: 2582-2160   •   Impact Factor: 9.24

A Widely Indexed Open Access Peer Reviewed Multidisciplinary Bi-monthly Scholarly International Journal

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Statistical Dependency Analysis of Multichannel Signals Using Probability and Random Process Techniques

Author(s) Dr. T Sudha, Ms. Kosuri Javali
Country India
Abstract The existence of multichannel signals is a common phenomenon that can be found in various engineering applications like electroencephalography (EEG), multiple-input multiple-output (MIMO) communication systems, and sensor arrays. Channels’ statistical dependency comprehension is the determining factor for effective signal interpretation, noise reduction, and system design. This study offers a framework based on probability and random process concepts for inter-channel dependency analysis by making use of classical statistics and signal processing instruments. Correlation, covariance, mutual information,
principal component analysis (PCA), cross-correlation, and coherence analysis are used to capture the relationships that are both linear and nonlinear, in time and frequency domains. Furthermore, a new method of lag-resolved mutual information and coherence fusion is presented that can uncover the hidden
temporal and spectral dependencies which would otherwise go undetected via the conventional correlation measures. To prove the efficiency of the suggested approach, MATLAB-based simulations have been conducted that not only reveal the clear periodic dependency patterns but also the dominant frequency
coupling across the different channels. All findings point to the benefit of using probabilistic dependency measures for multichannel signal analysis, which does not require the help of machine learning algorithms.
Keywords Multichannel signals, random processes, mutual information, coherence analysis, dependency analysis, MATLAB
Field Mathematics > Statistics
Published In Volume 7, Issue 6, November-December 2025
Published On 2025-12-28
DOI https://doi.org/10.36948/ijfmr.2025.v07i06.64802
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E-ISSN 2582-2160
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