Sparse component analysis paradigm of EEG signal and its feasibility

As a reliable signal processing and transmission method, sparse decomposition technology has been widely applied in many time-varying signal analysis and processing fields including EEG. Existing algorithms such as ICA and EMD often (almost always) bring significant discrepancy between their decomposition results and the real components, so it is difficult to estimate the waveform of the actual components by these algorithms. This paper provides an improved sparse performance index (SPI) by measuring the sparse distribution of the EEG samples, and introduces a new paradigm of component analysis and its optimizing function based on the sparse decomposition algorithm. We also prove by theory and practice that this paradigm can make the decomposition result tend to real components more effectively than traditional methods in the field of compositional analysis, which will provide considerable positive advantages to analysis of EEG and other time-varying signals.