Add to ORKGBrain decoding is the process of predicting cognitive states from medical data which consists of thousands of voxels and hundreds of samples. Features representing spatial and temporal relationships among neighboring voxels are discriminative and these relationships are estimated by solving regression for all samples of all voxels. Finding the nearest neighbors of all voxels and computing regression that includes matrix multiplication, addition and inverse with GPU implementation has a high speedup over CPU implementation
<div><p>Electroencephalogram (EEG)-based decoding human brain activity is challenging, owing to the ...
Brain-computer interface (BCI) is a promising technique which analyses and translates brain signals ...
SummaryRecent advances in neuroimaging allow mental states to be inferred from non-invasive data. In...
Description Compute Unified Device Architecture (CUDA) is a software platform for massively parallel...
We propose a method called Functional Mesh Model with Temporal Measurements (FMM-TM) to estimate a f...
In this study, we combine a voxel selection method with temporal mesh model to decode the discrimina...
One of the major drawbacks of brain decoding from the functional magnetic resonance images (fMRI) is...
Image texture extraction and analysis are fundamental steps in computer vision. In particular, consi...
The last two decades have seen tremendous advances in our understanding of human brain structure and...
Over the recent years, Electroencephalography (EEG) signal analysis has been found is one of the mos...
Abstract—In this paper a solution to identify the tumor suspect areas from the CAT scan and MR image...
How neurons influence each other's firing depends on the strength of synaptic connections among them...
The massively dynamic nature of human brain cannot be represented by considering only a collection o...
A brain-computer interface (BCI) translates the human's brain signals to give a second chance to neu...
Electroencephalogram (EEG)-based decoding human brain activity is challenging, owing to the low spat...
<div><p>Electroencephalogram (EEG)-based decoding human brain activity is challenging, owing to the ...
Brain-computer interface (BCI) is a promising technique which analyses and translates brain signals ...
SummaryRecent advances in neuroimaging allow mental states to be inferred from non-invasive data. In...
Description Compute Unified Device Architecture (CUDA) is a software platform for massively parallel...
We propose a method called Functional Mesh Model with Temporal Measurements (FMM-TM) to estimate a f...
In this study, we combine a voxel selection method with temporal mesh model to decode the discrimina...
One of the major drawbacks of brain decoding from the functional magnetic resonance images (fMRI) is...
Image texture extraction and analysis are fundamental steps in computer vision. In particular, consi...
The last two decades have seen tremendous advances in our understanding of human brain structure and...
Over the recent years, Electroencephalography (EEG) signal analysis has been found is one of the mos...
Abstract—In this paper a solution to identify the tumor suspect areas from the CAT scan and MR image...
How neurons influence each other's firing depends on the strength of synaptic connections among them...
The massively dynamic nature of human brain cannot be represented by considering only a collection o...
A brain-computer interface (BCI) translates the human's brain signals to give a second chance to neu...
Electroencephalogram (EEG)-based decoding human brain activity is challenging, owing to the low spat...
<div><p>Electroencephalogram (EEG)-based decoding human brain activity is challenging, owing to the ...
Brain-computer interface (BCI) is a promising technique which analyses and translates brain signals ...
SummaryRecent advances in neuroimaging allow mental states to be inferred from non-invasive data. In...