Email this article Updates on Electroencephalogram (EEG) Denoising Techniques
Abstract
Electroencephalogram (EEG) is an electrical signal which depicts brain activities; it is collected to analyse and study the brain activities from the scalps using electrodes. Several unwanted components called artifacts or noise contaminate the desired EEG signal during the raw signal collection. These components include eye blinking, environmental factors, power source, muscle movements, and heart rate affecting EEG processing and analysis. Therefore, it is necessary to eliminate or reduce these components in a process called EEG Denoising. Several denoising techniques have been proposed by researchers. This paper review these techniques and works carried out by researchers in that direction from the last decade to the present. This study also highlights the theoretical aspect of EEG signals. Findings reveal that there are still some drawbacks with conventional denoising techniques, such as the inability to denoise certain types of noise and loss of desired information resulting from discarding the unwanted components. In recent times, researchers have been combining conventional techniques with other modern techniques such as machine learning to improve their algorithm’s performance. Machine learning and deep learning techniques are a new trend in EEG denoising, which provides high performance and efficiency with an automated solution. From the analyses conducted on various articles reviewed in this study, comparative analysis with the advantage and disadvantages of each method have been highlighted and discussed with some recommendations for selecting a suitable technique based on a particular application and scenario.
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