Tag Archives: fMRI

Decoded Neurofeedback A New Tool For Matrix Style Effortless Automatic Learning

Education for many serves as the primary pathway to a better more prosperous life, but what happens when emerging technology changes how we learn and acquire new skills? Imagine a world where you could learn how to play the piano, pitch the perfect fastball or operate a new piece of complex machinery by simply having the information uploaded to your brain. Researchers at Boston University and the Computational Neuroscience Laboratories in Kyoto Japan, have demonstrated the ability for an individual to; “learn high-performance tasks with little or no conscious effort”. It’s called Decoded Neurofeedback or DecNef and according to its developers it could represent a paradigm shift education and training. By leveraging the power of fMRI researchers were able to; ” use decoded functional magnetic resonance imaging  to induce brain activity patterns to match a previously known target state and thereby improve performance on visual tasks. Think of a person watching a computer screen and having his or her brain patterns modified to match those of a high-performing athlete or modified to recuperate from an accident or disease.” (Science Daily) This research many believe could lead to the development of automated learning techniques that could fundamentally change the nature of education and training.


 
Read More
Vision Scientists Demonstrate Innovative Learning Method
‘Matrix’-Style Effortless Learning? Vision Scientists Demonstrate Innovative Learning Method
Perceptual Learning Incepted by Decoded fMRI Neurofeedback Without Stimulus Presentation

How Thought Identification Technology May Soon “Read” Your Mind

A machine that can read your mind, once thought of as science fiction is quickly becoming a reality. It’s called thought identification and according to researchers at Carnegie Mellon University the technology to identify basic thoughts is here. The possible applications for this technology is almost limitless and it is sure to spark debates legal and ethical. How our society will adapt to this and a number of other emerging technologies is yet to be known. What is known is that this technology will become better, faster and more portable in the years to come as the force of accelerating change takes hold.

How Technology May Soon “Read” Your Mind – 60 Minutes – CBS News

UC Berkeley Researchers Reconstruct YouTube Videos from Brain Activity

by Radford Castro of  lazytechguys.com

“Think Minority Report because this one is a trip. Now, the video you’re about to see is somewhat disturbing because what the university has accomplished is short of astounding. While the quality of the videos they recreate isn’t good at all, it’s still scary that an image can still be displayed.

How do they do this?

The study had the participants in an MRI machine for hours at a time watching YouTube videos. The data gathered by the MRI machine was used to create a computer model that matched the features of the video like colors, shapes, and movements with the brain activity.

The video was recreated by looking at slight changes in blood flow to visual areas of the brain were used to predict what was on the screen at the time. The team thinks that one day the tech could be used to broadcast imagery that plays in the mind independent from vision. You know what? Just watch it.” Source: LTG

Read More:
Lazy Tech Guys: http://www.lazytechguys.com/news/uc-berkeley-has-found-a-way-to-pull-videos-from-your-brain/

For more information about this work: http://gallantlab.org

For the paper (Nishimoto et al., 2011, Current Biology) go to:http://dx.doi.org/10.1016/j.cub.2011.08.031

Reconstructing visual experiences from brain activity evoked by natural movies (Current Biology in press). This paper presents the first successful approach for reconstructing natural movies from brain activity.

Encoding and decoding in fMRI (Neuroimage 2010, PDF 841KB). This paper reviews the current state of “brain decoding” research, and advocates one particularly powerful approach.

Bayesian reconstruction of natural images from human brain activity (Neuron 2009, PDF 3.7MB). This paper presents the first successful approach for reconstructing natural images from brain activity.

Identifying natural images from human brain activity (Nature 2008, PDF 5.4MB). This landmark paper shows that far more information can be recovered from brain activity than was thought previously.

Modeling low-frequency fluctuation and hemodynamic response timecourse in event-related fMRI (Human Brain Mapping 2008, PDF 717KB). This technical paper focuses on optimal quantitative modeling of fMRI data.

Topographic organization in and near human visual areas V4 (Journal of Neuroscience 2007, PDF 4.5MB). This paper provides a detailed retinotopic mapping study of early human visual areas.