AIMC Topic: Magnetoencephalography

Clear Filters Showing 51 to 60 of 71 articles

The temporal evolution of conceptual object representations revealed through models of behavior, semantics and deep neural networks.

NeuroImage
Visual object representations are commonly thought to emerge rapidly, yet it has remained unclear to what extent early brain responses reflect purely low-level visual features of these objects and how strongly those features contribute to later categ...

Relating dynamic brain states to dynamic machine states: Human and machine solutions to the speech recognition problem.

PLoS computational biology
There is widespread interest in the relationship between the neurobiological systems supporting human cognition and emerging computational systems capable of emulating these capacities. Human speech comprehension, poorly understood as a neurobiologic...

Convolutional neural network-based encoding and decoding of visual object recognition in space and time.

NeuroImage
Representations learned by deep convolutional neural networks (CNNs) for object recognition are a widely investigated model of the processing hierarchy in the human visual system. Using functional magnetic resonance imaging, CNN representations of vi...

Comparison of deep neural networks to spatio-temporal cortical dynamics of human visual object recognition reveals hierarchical correspondence.

Scientific reports
The complex multi-stage architecture of cortical visual pathways provides the neural basis for efficient visual object recognition in humans. However, the stage-wise computations therein remain poorly understood. Here, we compared temporal (magnetoen...

Dynamics of scene representations in the human brain revealed by magnetoencephalography and deep neural networks.

NeuroImage
Human scene recognition is a rapid multistep process evolving over time from single scene image to spatial layout processing. We used multivariate pattern analyses on magnetoencephalography (MEG) data to unravel the time course of this cortical proce...

A multi-layer network approach to MEG connectivity analysis.

NeuroImage
Recent years have shown the critical importance of inter-regional neural network connectivity in supporting healthy brain function. Such connectivity is measurable using neuroimaging techniques such as MEG, however the richness of the electrophysiolo...

Supervised learning for neural manifold using spatiotemporal brain activity.

Journal of neural engineering
OBJECTIVE: Determining the means by which perceived stimuli are compactly represented in the human brain is a difficult task. This study aimed to develop techniques for the construction of the neural manifold as a representation of visual stimuli.

A study on a robot arm driven by three-dimensional trajectories predicted from non-invasive neural signals.

Biomedical engineering online
BACKGROUND: A brain-machine interface (BMI) should be able to help people with disabilities by replacing their lost motor functions. To replace lost functions, robot arms have been developed that are controlled by invasive neural signals. Although in...

Attentional Enhancement of Auditory Mismatch Responses: a DCM/MEG Study.

Cerebral cortex (New York, N.Y. : 1991)
Despite similar behavioral effects, attention and expectation influence evoked responses differently: Attention typically enhances event-related responses, whereas expectation reduces them. This dissociation has been reconciled under predictive codin...

Multimodal personalization of transcranial direct current stimulation for modulation of sensorimotor integration.

NeuroImage
Transcranial direct current stimulation (tDCS) for the modulation of smooth pursuit eye movements provides an ideal model for investigating sensorimotor integration. Within neural networks subserving smooth pursuit, visual area V5 is a core hub where...