This project was focused on the COVID-19 Coronavirus as a case study.
OSET is a collection of electrophysiological data and open source codes for biological signal generation, modeling, processing, and filtering, originally released in June 2006 and continuously updated. The latest version (the Pi version) can be cloned from my gitlab or github mirror repositories.
The instantaneous phase (IP) and instantaneous frequency (IF) of the electroencephalogram (EEG) are notable complements for the EEG spectrum. The calculation of these parameters commonly includes narrow-band filtering, followed by the calculation of the signal’s analytical form. The calculation of the IP and IF is highly susceptible to the filter parameters and background noise level, especially in low analytical signal amplitudes. In a series of research we have proposed a robust statistical framework for EEG IP/IF estimation and analysis. See my publications page for the full list of related papers. Here’s a short list to start with:
Interpretive Signal Processing (ISP) is an ad hoc technique for customizing signal processing algorithms for non-numeric data. Genomic data such as DNA or protein sequences are examples of such data. Contrary to the conventional approach of coding and decoding non-numeric data to numeric values, the main idea in ISP is to interpret signal processing algorithm as they are and to taylor similar operators for the direct manipulation of non-numeric data. We have studied two cases of ISP in our previous research: