Description: Biological signal processing refers to the analysis and interpretation of signals generated by biological systems, such as electrical signals from the brain, heart, or muscles. These signals are fundamental for understanding the internal functioning of organisms and can be measured through various techniques, such as electroencephalography (EEG), electromyography (EMG), and electrocardiography (ECG). The goal of processing these signals is to extract relevant information that allows for diagnosing medical conditions, monitoring health, and developing human-machine interfaces. This field combines knowledge from biology, engineering, and mathematics, using algorithms and computational models to interpret complex data. The relevance of biological signal processing lies in its ability to improve people’s quality of life, facilitating the development of technologies that enable rehabilitation, control of prosthetics, and communication in patients with disabilities. Furthermore, advancements in neuromorphic computing have opened new possibilities for the efficient processing of these signals, mimicking the functioning of the human brain and allowing for faster and more accurate analysis.
History: Biological signal processing began to develop in the 1960s when techniques such as electroencephalography (EEG) were introduced to study brain activity. Over the years, advancements in technology have enabled the creation of more sophisticated devices for capturing and analyzing biological signals. In the 1980s, digital signal processing (DSP) algorithms began to be used to enhance the quality of the obtained signals. With the advancement of computing and the development of artificial intelligence techniques in the 21st century, biological signal processing has experienced exponential growth, allowing for more complex and precise applications in the medical and research fields.
Uses: Biological signal processing has multiple applications in the medical field, such as diagnosing neurological, cardiac, and muscular diseases. It is used in patient monitoring in intensive care units, as well as in the rehabilitation of individuals with motor disabilities. Additionally, it is applied in the development of brain-computer interfaces, allowing users to control devices with their thoughts. In the research field, it is used to study human and animal behavior, as well as to develop new therapies and treatments.
Examples: An example of biological signal processing is the use of electroencephalograms (EEG) to diagnose disorders such as epilepsy. Another case is the use of electrocardiograms (ECG) to monitor heart health and detect arrhythmias. In the rehabilitation field, EMG-controlled prosthetics have been developed that allow users to move artificial limbs through muscle activity. Additionally, brain-computer interfaces have been used in research to help individuals with paralysis communicate and control electronic devices.
