Single-trial Analysis for empirical MEG data

Independent component analysis for unaveraged single-trial MEG data decomposition and single-dipole source localization

This paper presents a novel method for decomposing and localizing unaveraged single-trial magnetoencephalographic data based on the independent component analysis (ICA) approach associated with preand post-processing techniques. In the pre-processing stage, recorded single-trial raw data are 6rst decomposed into uncorrelated signals with the reduction of high-power additive noise. In the stage ...

Spike train analysis for single trial data

Independent component analysis for noisy data -- MEG data analysis

Independent component analysis (ICA) is a new, simple and powerful idea for analyzing multi-variant data. One of the successful applications is neurobiological data analysis such as electroencephalography (EEG), magnetic resonance imaging (MRI), and magnetoencephalography (MEG). However, many problems remain. In most cases, neurobiological data contain a lot of sensor noise, and the number of i...

ICA-based spatiotemporal approach for single-trial analysis of postmovement MEG beta synchronization.

The extraction of event-related oscillatory neuromagnetic activities from single-trial measurement is challenging due to the non-phase-locked nature and variability from trial to trial. The present study presents a method based on independent component analysis (ICA) and the use of a template-based correlation approach to extract Rolandic beta rhythm from magnetoencephalographic (MEG) measureme...

Single-trial detection in EEG and MEG: Keeping it linear

Conventional electroencephalography (EEG) and magnetoencephalography (MEG) analysis often rely on averaging over multiple trials to extract statistically relevant di7erences between two or more experimental conditions. We demonstrate that by linearly integrating information over multiple spatially distributed sensors within a prede9ned time window, one can discriminate conditions on a trial-by-...