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EMG signals are generally contaminated by various kinds of noises in a heterogeneous way. Among these various noises, major issue is the proper removal of Additive White Gaussian Noise (AWGN), whose spectral components overlay the spectrum of EMG signals; making its analysis troublesome. This paper presents an approach for AWGN removal from the EMG signal using Canonical Correlation Analysis (CCA). In this approach, CCA is first performed on the noisy EMG signals to break them into various canonical components followed by Morphological Filtering. Herein, a square-shaped structuring element is deployed which filters the canonical components. After that, the outcomes of the proposed methodology are contemplated with the approaches adopted in CCA-Gaussian filtering and CCA-thresholding. Outcomes of simulations show that the preprocessing approach used in this work suppresses …