A Polarity Alignment Method for Group-Averaging of Event-Related Neural Signals at Source Level in MEG Beamforming Applications

2021 ◽  
Author(s):  
Audrey Doualot ◽  
André Achim
Keyword(s):  
Alignment Method ◽  
Neural Signals ◽  
Source Level ◽  
Group Averaging

scholarly journals Consistency of local activation parameters at sensor- and source-level in neural signals

2020 ◽  
Vol 17 (5) ◽  
pp. 056020
Author(s):  
Víctor Rodríguez-González ◽  
Carlos Gómez ◽  
Yoshihito Shigihara ◽  
Hideyuki Hoshi ◽  
Marcos Revilla-Vallejo ◽  
...  
Keyword(s):  
Activation Parameters ◽  
Neural Signals ◽  
Local Activation ◽  
Source Level

scholarly journals Research on initial alignment method of vehicle-mounted SINS based on reverse navigation

2021 ◽  
Vol 1846 (1) ◽  
pp. 012075
Author(s):  
Chen Yang ◽  
Yuanwen Cai ◽  
Chaojun Xin ◽  
Meiling Shi
Keyword(s):  
Alignment Method ◽  
Initial Alignment

scholarly journals Jumping over baselines with new methods to predict activation maps from resting-state fMRI

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Eric Lacosse ◽  
Klaus Scheffler ◽  
Gabriele Lohmann ◽  
Georg Martius
Keyword(s):  
Resting State ◽  
Brain Activity ◽  
Resting State Fmri ◽  
Behavioral Differences ◽  
Large Variability ◽  
Single Vertex ◽  
Spontaneous Brain Activity ◽  
New Methods ◽  
Group Averaging ◽  
Proposed Modification

AbstractCognitive fMRI research primarily relies on task-averaged responses over many subjects to describe general principles of brain function. Nonetheless, there exists a large variability between subjects that is also reflected in spontaneous brain activity as measured by resting state fMRI (rsfMRI). Leveraging this fact, several recent studies have therefore aimed at predicting task activation from rsfMRI using various machine learning methods within a growing literature on ‘connectome fingerprinting’. In reviewing these results, we found lack of an evaluation against robust baselines that reliably supports a novelty of predictions for this task. On closer examination to reported methods, we found most underperform against trivial baseline model performances based on massive group averaging when whole-cortex prediction is considered. Here we present a modification to published methods that remedies this problem to large extent. Our proposed modification is based on a single-vertex approach that replaces commonly used brain parcellations. We further provide a summary of this model evaluation by characterizing empirical properties of where prediction for this task appears possible, explaining why some predictions largely fail for certain targets. Finally, with these empirical observations we investigate whether individual prediction scores explain individual behavioral differences in a task.

Dissociated neural signals of conflict and surprise in effortful decision Making: Theta activity reflects surprise while alpha and beta activity reflect conflict

2021 ◽  
Vol 155 ◽  
pp. 107793
Author(s):  
Chad C. Williams ◽  
Thomas D. Ferguson ◽  
Cameron D. Hassall ◽  
Bruce Wright ◽  
Olave E. Krigolson
Keyword(s):  
Decision Making ◽  
Theta Activity ◽  
Beta Activity ◽  
Neural Signals

scholarly journals Divergence of fMRI and neural signals in V1 during perceptual suppression in the awake monkey

2008 ◽  
Vol 11 (10) ◽  
pp. 1193-1200 ◽  
Author(s):  
Alexander Maier ◽  
Melanie Wilke ◽  
Christopher Aura ◽  
Charles Zhu ◽  
Frank Q Ye ◽  
...  
Keyword(s):  
Neural Signals ◽  
Awake Monkey

Anti-swaying self-alignment method based on observation vector inertia for SINS under latitude uncertainty

Measurement ◽  
2021 ◽  
pp. 109250
Author(s):  
Hongliang Zhang ◽  
Yilan Zhou ◽  
Tengchao Huang ◽  
Lei Wang
Keyword(s):  
Alignment Method ◽  
Observation Vector
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