Electroencephalogram (EEG) is widely used for monitoring, diagnosis purposes and also for study of brain's physiological, mental and functional abnormalities. Processing of information by the brain is reflected in dynamical changes of the electrical activity in time, frequency, and space. EEG signal processing tends to describe and quantify these variations in such a way that they are localized in temporal, spectral and spatial domain. Here we use multi-way (Tensor) analysis for localizing the EEG events. We used EMD process for decomposing EEG into distinct oscillatory modes, which are then mapped to TF plane using the near optimal Reassigned Spectrogram. Temporal, Spatial and Spectral information of the Multichannel EEG are then used to generate a three-way Frequency-Time-Space EEG tensor. Exploiting EMD also enables us to detrend the EEG recordings. Simulation results on both synthetic and real EEG data show that tensor analysis greatly improve separation and localization of overlapping events in EEG and it could be effectively exploited for detecting and characterizing the evoked potentials.
The -norm regularized least square technique has been effectively exploited for sparse reconstruction problems. However, the choice of an optimum regularization parameter in the optimization routine still remains a challenge. In this paper we propose a new criterion which is based on MNDL, a new method for optimum subspace selection in data representation, to select the optimum regularization parameter utilizing -regularized leastsquares. Simulations are done for combined model order selection and parameter estimation for the ubiquitous sinusoids-in-noise model. The results show that the MNDL based regularization parameter selection outperforms the state of the art methods that use MDL for the correct estimation of number of components in the signal.
MisMatch Negativity (MMN) is a small event-related potential (ERP) that provide an index of sensory learning and perceptual accuracy for the cognitive research. Group-level analysis plays an important role for detecting differences at group or condition level, especially when the signal-to-noise ratio is low. Tensor factorization has provided a framework for group-level analysis of ERPs by exploiting more information of brain responses in more domains simultaneously. A 4-way ERP tensor of time × frequency × channel × subjects/condition is generated and decomposed via PARAFAC. A crucial step after PARAFAC decomposition is to select the component that corresponds to the event of interest and moreover differentiates the two groups\conditions. This is usually done manually, which is tedious when the number of components is high. Here we propose a technique to select the multi-domain feature of an ERP among all extracted features by a template matching approach, that uses the MMN temporal and spectral signatures. Following a statistical test, the selected feature significantly discriminated subjects for the two experimental conditions.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.