Implementation of Adaptive Filtering for EEG Signal Denoising

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Implementation of an adaptive filter using the Least Mean Squares (LMS) algorithm for intracavitary radiotherapy applications. The primary objective is noise removal from EEG signals. The LMS filter is applied in three sequential stages: the first stage eliminates ECG noise, the second stage removes EOG noise, and the final stage attenuates 60 Hz power line interference.

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This article presents the application of an adaptive filter based on the Least Mean Squares (LMS) algorithm for EEG signal denoising. The algorithm aims to remove noise artifacts from electroencephalogram signals through sequential processing stages. The LMS filter architecture employs distinct reference inputs at three ordered stages: the first stage eliminates electrocardiogram (ECG) interference, the second stage cancels electrooculogram (EOG) artifacts, and the third stage suppresses 60 Hz power line noise. Implementation typically involves defining separate filter lengths and step-size parameters for each noise type, with the filter weights updated recursively using the LMS weight adaptation formula: w(n+1) = w(n) + μ·e(n)·x(n), where μ denotes the convergence factor. This multi-stage approach yields cleaner, more precise signals, providing reliable data for subsequent biomedical research and analysis.

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