Encoder with the Empirical Mode Decomposition (EMD) to remove muscle artefacts from EEG signal

TL;DR

This paper presents a novel EEG artifact removal method combining Empirical Mode Decomposition with machine learning-based interpolation to better preserve signal integrity and improve artifact removal effectiveness.

Contribution

It introduces a new approach that enhances EMD artifact removal by integrating machine learning for interpolation, addressing limitations of existing techniques.

Findings

Effective removal of muscle artifacts from EEG signals.

Preservation of original signal frequency components.

Validated improved signal quality through experimental results.

Abstract

This paper introduces a novel method for effectively removing artifacts from EEG signals by combining the Empirical Mode Decomposition (EMD) method with a machine learning architecture. The proposed method addresses the limitations of existing artifact removal techniques by enhancing the EMD method through interpolation of the upper and lower. For conventional artifact removal methods, the EMD technique is commonly employed. However, the challenge lies in accurately interpolating the missing components of the signal while preserving its inherent frequency components. To overcome this limitation, we incorporated machine learning technique, which enables us to carefully handle the interpolation process without directly manipulating the data. The key advantage of our approach lies in the preservation of the natural characteristics of the EEG signal during artifact removal. By utilizing machine learning for interpolation, we ensure that the average component obtained through the EMD method retains the crucial frequency components of the original signal. This preservation is essential for maintaining the integrity and fidelity of the EEG data, allowing for accurate analysis and interpretation. The results obtained from our evaluation serve to validate the effectiveness of our approach and pave the way for further advancements in EEG signal processing and analysis.