Multi-domain feature selection in auditory MisMatch Negativity via PARAFAC-based template matching approach.

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.

A new approach for SSVEP detection using PARAFAC and canonical correlation analysis.

This paper presents a new way for automatic detection of SSVEPs through correlation analysis between tensor models. 3-way EEG tensor of channel × frequency × time is decomposed into constituting factor matrices using PARAFAC model. PARAFAC analysis of EEG tensor enables us to decompose multichannel EEG into constituting temporal, spectral and spatial signatures. SSVEPs characterized with localized spectral and spatial signatures are then detected exploiting a correlation analysis between extracted signatures of the EEG tensor and the corresponding simulated signatures of all target SSVEP signals. The SSVEP that has the highest correlation is selected as the intended target. Two flickers blinking at 8 and 13 Hz were used as visual stimuli and the detection was performed based on data packets of 1 second without overlapping. Five subjects participated in the experiments and the highest classification rate of 83.34% was achieved, leading to the Information Transfer Rate (ITR) of 21.01 bits/min.

Localization of brain activities using multiway analysis of EEG tensor via EMD and reassigned TF representation.

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.