AIMC Topic: Photic Stimulation
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Supervised learning for neural manifold using spatiotemporal brain activity.
Journal of neural engineering
Nov 18, 2015
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
Nov 3, 2015
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...
Real time unsupervised learning of visual stimuli in neuromorphic VLSI systems.
Scientific reports
Oct 14, 2015
Neuromorphic chips embody computational principles operating in the nervous system, into microelectronic devices. In this domain it is important to identify computational primitives that theory and experiments suggest as generic and reusable cognitiv...
Increasing N200 Potentials Via Visual Stimulus Depicting Humanoid Robot Behavior.
International journal of neural systems
Oct 9, 2015
Achieving recognizable visual event-related potentials plays an important role in improving the success rate in telepresence control of a humanoid robot via N200 or P300 potentials. The aim of this research is to intensively investigate ways to induc...
Optimized face recognition algorithm using radial basis function neural networks and its practical applications.
Neural networks : the official journal of the International Neural Network Society
Jun 5, 2015
In this study, we propose a hybrid method of face recognition by using face region information extracted from the detected face region. In the preprocessing part, we develop a hybrid approach based on the Active Shape Model (ASM) and the Principal Co...
Language-Model Assisted Brain Computer Interface for Typing: A Comparison of Matrix and Rapid Serial Visual Presentation.
IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
Mar 11, 2015
Noninvasive electroencephalography (EEG)-based brain-computer interfaces (BCIs) popularly utilize event-related potential (ERP) for intent detection. Specifically, for EEG-based BCI typing systems, different symbol presentation paradigms have been ut...
Evoked Potentials, Visual
Task Performance and Analysis
Photic Stimulation
Communication Devices for People with Disabilities
Word Processing
Natural Language Processing
Electroencephalography
Pattern Recognition, Automated
Brain-Computer Interfaces
Sensitivity and Specificity
Male
Models, Neurological
Language
Female
Reproducibility of Results
Humans
Adult
Machine Learning
Algorithms
Scene recognition by manifold regularized deep learning architecture.
IEEE transactions on neural networks and learning systems
Jan 22, 2015
Scene recognition is an important problem in the field of computer vision, because it helps to narrow the gap between the computer and the human beings on scene understanding. Semantic modeling is a popular technique used to fill the semantic gap in ...
Inhibition facilitates direction selectivity in a noisy cortical environment.
Journal of computational neuroscience
Nov 18, 2014
In a broad class of models, direction selectivity in primary visual cortical neurons arises from the linear summation of spatially offset and temporally lagged inputs combined with a spike threshold. Here, we characterize the robustness of this class...
Learning to track multiple targets.
IEEE transactions on neural networks and learning systems
Jul 14, 2014
Monocular multiple-object tracking is a fundamental yet under-addressed computer vision problem. In this paper, we propose a novel learning framework for tracking multiple objects by detection. First, instead of heuristically defining a tracking algo...
Decoding objects of basic categories from electroencephalographic signals using wavelet transform and support vector machines.
Brain topography
May 17, 2014
Decoding and classification of objects through task-oriented electroencephalographic (EEG) signals are the most crucial goals of recent researches conducted mainly for brain-computer interface applications. In this study we aimed to classify single-t...
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