AIMC Topic: Photic Stimulation

Clear Filters Showing 191 to 200 of 244 articles

Models of visual categorization.

Wiley interdisciplinary reviews. Cognitive science
Visual categorization refers to our ability to organize objects and visual scenes into discrete categories. It is an essential skill as it allows us to distinguish friend from foe or edible versus poisonous food. Understanding how the visual system c...

Atoms of recognition in human and computer vision.

Proceedings of the National Academy of Sciences of the United States of America
Discovering the visual features and representations used by the brain to recognize objects is a central problem in the study of vision. Recently, neural network models of visual object recognition, including biological and deep network models, have s...

Multivoxel Object Representations in Adult Human Visual Cortex Are Flexible: An Associative Learning Study.

Journal of cognitive neuroscience
Learning associations between co-occurring events enables us to extract structure from our environment. Medial-temporal lobe structures are critical for associative learning. However, the role of the ventral visual pathway (VVP) in associative learni...

Modality-independent representations of small quantities based on brain activation patterns.

Human brain mapping
Machine learning or MVPA (Multi Voxel Pattern Analysis) studies have shown that the neural representation of quantities of objects can be decoded from fMRI patterns, in cases where the quantities were visually displayed. Here we apply these technique...

Visual Tracking Based on an Improved Online Multiple Instance Learning Algorithm.

Computational intelligence and neuroscience
An improved online multiple instance learning (IMIL) for a visual tracking algorithm is proposed. In the IMIL algorithm, the importance of each instance contributing to a bag probability is with respect to their probabilities. A selection strategy ba...

A Fuzzy Integral Ensemble Method in Visual P300 Brain-Computer Interface.

Computational intelligence and neuroscience
We evaluate the possibility of application of combination of classifiers using fuzzy measures and integrals to Brain-Computer Interface (BCI) based on electroencephalography. In particular, we present an ensemble method that can be applied to a varie...

Improved Neural Signal Classification in a Rapid Serial Visual Presentation Task Using Active Learning.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
The application space for brain-computer interface (BCI) technologies is rapidly expanding with improvements in technology. However, most real-time BCIs require extensive individualized calibration prior to use, and systems often have to be recalibra...

Towards a symbiotic brain-computer interface: exploring the application-decoder interaction.

Journal of neural engineering
OBJECTIVE: State of the art brain-computer interface (BCI) research focuses on improving individual components such as the application or the decoder that converts the user's brain activity to control signals. In this study, we investigate the intera...

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.

Measuring empathy for human and robot hand pain using electroencephalography.

Scientific reports
This study provides the first physiological evidence of humans' ability to empathize with robot pain and highlights the difference in empathy for humans and robots. We performed electroencephalography in 15 healthy adults who observed either human- o...