Consensus Matching Pursuit of Multi-Trial Biosignals, with Application to Brain Signals
نویسندگان
چکیده
Time-frequency representations are commonly used to analyze the oscillatory nature of bioelectromagnetic signals. There is a growing interest in sparse representations, where the data is described using few components. In this study, we adapt the Matching Pursuit of Mallat and Zhang for biosignals consisting of a series of variations around a similar pattern, with emphasis on multi-trial datasets encountered in MEG and EEG. The general principle of Matching Pursuit (MP) is to iteratively subtract from the signal its projection on the atom selected from a dictionary. The originality of our method is to select each atom using a voting technique that is robust to variability, and to subtract it by adapting the parameters to each trial. Because it is designed to handle inter-trial variability using a voting technique, the method is called Consensus Matching Pursuit (CMP). The method is validated on both simplified and realistic simulations, and on two real datasets (intracerebral EEG and scalp EEG ). We also compare our method to two other multi-trial MP algorithms: Multivariate MP (MMP) and Induced activity MP (IMP). CMP is shown to be able to sparsely reveal the structure present in the data, and to be robust to variability (jitter) across trials.
منابع مشابه
PMU-Based Matching Pursuit Method for Black-Box Modeling of Synchronous Generator
This paper presents the application of the matching pursuit method to model synchronous generator. This method is useful for online analysis. In the proposed method, the field voltage is considered as input signal, while the terminal voltage and active power of the generator are output signals. Usually, the difference equation with a second degree polynomial structure is used to estimate the co...
متن کاملApproximating the Time-Frequency Representation of Biosignals with Chirplets
A new member of the Cohen’s class time-frequency distribution is proposed. The kernel function is determined adaptively based on the signal of interest. The kernel preserves the chirp-like components while removing interference terms generated due to the quadratic characteristic of Wigner-Ville distribution. This approach is based on the chirplet as an underlying model of biomedical signals. We...
متن کاملApplication of Composite Dictionary Multi-Atom Matching in Gear Fault Diagnosis
The sparse decomposition based on matching pursuit is an adaptive sparse expression method for signals. This paper proposes an idea concerning a composite dictionary multi-atom matching decomposition and reconstruction algorithm, and the introduction of threshold de-noising in the reconstruction algorithm. Based on the structural characteristics of gear fault signals, a composite dictionary com...
متن کاملMulti-electrode arrays technology for the non-invasive recording of neural signals: a review article
The recording of electrophysiological activities of brain neurons in the last half-century has been considered as one of the effective tools for the development of neuroscience. One of the techniques for recording the activity of nerve cells is the multi-electrode arrays (MEAs). Microelectrode arrays (MEAs) are usually employed to record electrical signals from electrogenic cells like neurons o...
متن کاملTime-frequency analysis using the matching pursuit algorithm applied to seizures originating from the mesial temporal lobe.
OBJECTIVES The ability to analyze patterns of recorded seizure activity is important in the localization and classification of seizures. Ictal evolution is typically a dynamic process with signals composed of multiple frequencies; this can limit or complicate methods of analysis. The recently-developed matching pursuit algorithm permits continuous time-frequency analyses, making it particularly...
متن کامل