Sentence examples for neural sources from inspiring English sources

The aim of source reconstruction is to localize the neural sources underlying the signals measured at the sensor level.

This work presents a new dipolar method to estimate the neural sources from separate or combined EEG and MEG data.

A third relevant aspect is that the final probability estimate is a result of the probabilistic integration of the neural sources of numerous models.

A second important aspect is the consideration of several banks of filters that simultaneously estimate and integrate the neural sources of different models.

The most common approach to reduce muscle artifacts in electroencephalographic signals is to linearly decompose the signals in order to separate artifactual from neural sources, using one of several variants of independent component analysis (ICA).

The novelty lies in the simultaneous estimation and integration of neural sources from different dynamic models with different parameters, leading to a dynamic multi-model solution for the EEG/MEG source localization problem.

Since the underlying neuronal sources and their interactions are unknown in real MEG data, we demonstrate the performance of the proposed beamforming method in a novel simulation scheme, where intracranial recordings from a macaque monkey are used as neural sources to simulate realistic MEG signals.

This approach seems justified since neighboring electrodes record from overlapping neural sources.

The underlying neural sources were estimated by means of the least square minimum-norm-estimation (L2-MNE) approach.

Figure 2 shows the grand-average ERD data with no cleaning and the same data cleaned by removing all non-neural sources for each method, except SSD for which we retained the five components with highest SNR.

The strong non-neural source of intra- and intersubject variability of BOLD response during the block design compared to event-related task indicates that study designs optimizing for statistical power through enhancement of the BOLD contrast (for, e.g., block design) can be affected by enhancement of non-neural sources of BOLD variability.