Non Invasive

Non-invasive techniques aim to extract information from the human body without the need for surgery or invasive procedures. Current research focuses on decoding various physiological signals, including brain activity (EEG, fMRI, fNIRS) and other biosignals (gait patterns, heart rate from facial videos), using advanced machine learning models such as deep neural networks (including transformers, convolutional neural networks, and recurrent neural networks), and ensemble learning methods. These advancements hold significant promise for improving healthcare diagnostics (e.g., early cancer detection, epilepsy monitoring, sleep apnea diagnosis), creating more effective brain-computer interfaces (BCIs), and enabling personalized medicine through non-invasive monitoring and analysis of physiological data.