Abstract
This survey paper offers a comprehensive review of the recent
advances and applications of Machine Learning (ML) approaches in the
interdisciplinary field of bioengineering, specifically in the realm of
biosignal processing. Biosignals, including electroencephalograms (EEG),
electrocardiograms (ECG), and electromyograms (EMG), are inherently
complex, presenting significant challenges such as noise, artifacts,
variability, and nonlinearity in their processing. However, ML has shown
promise in overcoming these hurdles, enabling the extraction of useful
features and insights from these signals. The paper outlines how ML is
leveraged for processing, analyzing, classifying, and interpreting
biosignals for various applications, such as diagnosis, monitoring,
rehabilitation, and brain-computer interfaces. Additionally, it
discusses the ongoing challenges and potential future directions of ML
applications in this field. Through this review, we aim to highlight the
critical role of ML in enabling adaptive, personalized, and intelligent
systems that interact with biosignals in real-time, with potential
implications for improving patient outcomes in various medical
conditions.
, 
