The use of electrophysiological signals (ExG), such as electroencephalograms (EEG), electromyograms (EMG), electrocardiograms (EKG), electrooculograms (EOG), and electrocorticograms (ECoG), in the study of human physiology has become increasingly prevalent, fostering a deeper understanding of the complex mechanisms underlying the body's diverse functions. These biosignals, captured from different body sites, provide critical insight into the electrical activities of the brain, muscles, heart, eye movement, and cerebral cortex, thus offering a unique window into real-time physiological activities. However, the practical use of these signals necessitates advanced signal processing algorithms to clean, interpret, and make sense of the often noisy and complex raw data.
As part of our research, we are developing and employing sophisticated signal processing algorithms to process and extract key features from these ExG signals. The primary focus of our research lies in utilizing techniques such as Fourier transform, wavelet transform, principal component analysis, and machine learning models to discern useful information from the signals, thereby enabling us to identify patterns related to health and disease states. These algorithms' ability to separate useful signals from noise, reduce dimensionality, and classify patterns not only enhances our understanding of the underlying physiology but also holds promise in advancing diagnostic and therapeutic strategies. By capitalizing on the rich information hidden in ExG signals, our research aims to contribute to personalized medicine and improve health outcomes.
As part of our research, we are developing and employing sophisticated signal processing algorithms to process and extract key features from these ExG signals. The primary focus of our research lies in utilizing techniques such as Fourier transform, wavelet transform, principal component analysis, and machine learning models to discern useful information from the signals, thereby enabling us to identify patterns related to health and disease states. These algorithms' ability to separate useful signals from noise, reduce dimensionality, and classify patterns not only enhances our understanding of the underlying physiology but also holds promise in advancing diagnostic and therapeutic strategies. By capitalizing on the rich information hidden in ExG signals, our research aims to contribute to personalized medicine and improve health outcomes.