Sunday, May 8, 2016

MRI BCI - Internet of visible thought

My publication at Siggraph in '98 proposed conversion of brainwave algorithms to create imagery and sound, plus AI to drive the imagery for guiding VR subject towards a target brainwave state for deepening immersion into virtual environments.

I've recently been writing ideas surrounding creation of an image-based language to introduce a new communication paradigm.

1. Matching mind images to an image/ video bank

2. Mapping image/impression-based communication forms which can be triggered by voice command

Highlights from the article (below):

Her vision is broad and sweeping: it runs from a new generation of extremely high-resolution, affordable MRI machines for early detection of cancer, heart disease, and more, to a far-out time (or maybe not so far-out) when machines can read people’s minds and people can communicate—with each other and maybe even with animals—via thoughts.

The idea “leverages the tools of our times,” Jepsen says, citing advances in everything from physics to optoelectronics to consumer electronics to big data and A.I. that can be combined to shrink the size, improve the functionality, and lower the cost of MRI. “I could no longer wait. I’m still writing up the patents. But I am incredibly excited to strike off on this direction,” she says.“My big bet is we can use that manufacturing infrastructure to create the functionality of a $5 million MRI machine in a consumer electronics price-point wearable. And the implications of that are so big.” She says every doctor’s office in the world could afford these wearable devices and use them for early detection of neurodegenerative disease, cancer, cardiovascular disease, internal bleeding, blood clots, and more.

About Mary Lou Jepson

http://www.maryloujepsen.com

http://www.maryloujepsen.com/#!resume/c46c

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 http://www.xconomy.com/san-francisco/2016/05/06/mary-lou-jepsen-on-life-post-facebook-and-new-startup-open-water/

Mary Lou Jepsen on Life Post-Facebook and New Startup, “Open Water”

I had long planned a phone call with Mary Lou Jepsen for this afternoon—a prep session for a chat I will be doing with her a week from Monday night at Xconomy’s Napa Summit, where she is the featured dinner speaker. It was to be a normal prep chat until I got to work this morning and learned that CNET, Engadget, and Tech Insider had all reported that the technology visionary was planning to leave her post as executive director of engineering for Facebook and Oculus, to focus on a new startup. It turned out she had talked about her plans last night during a keynote speech at the Women of Vision Awards banquet in Santa Clara, CA—and the media outlets had all seized on the news.

“I was actually really surprised anybody picked that up,” Jepsen told me (showing she doesn’t fully understand what a big deal she is). So I took advantage of the call to ask her more. Some of our talk was off the record, but much of it was on the record, including quite a bit about her new plans and the thinking behind them.

Her vision is broad and sweeping: it runs from a new generation of extremely high-resolution, affordable MRI machines for early detection of cancer, heart disease, and more, to a far-out time (or maybe not so far-out) when machines can read people’s minds and people can communicate—with each other and maybe even with animals—via thoughts.

The idea “leverages the tools of our times,” Jepsen says, citing advances in everything from physics to optoelectronics to consumer electronics to big data and A.I. that can be combined to shrink the size, improve the functionality, and lower the cost of MRI. “I could no longer wait. I’m still writing up the patents. But I am incredibly excited to strike off on this direction,” she says.

The startup, whose name has not previously been released as far as I can tell, is called Open Water (it could also be OpenWater, “not sure yet…either is OK for now,” she says). “Peter Gabriel gave me the name. He is a great advisor,” Jepsen says. In particular, she was inspired by this article he wrote for Edge.org, called Open Water–The Internet of Visible Thought, in which he credited Jepsen for introducing him “to the potential of brain reading devices.”

Jepsen says she can’t talk about funding and more specific plans for Open Water yet, and that she will remain at Facebook until August. But here are some highlights of what she could say:

“What I try to do is make things that everybody knows are utterly, completely impossible—I try to make them possible,” Jepsen sums up. She does that by leveraging what she calls her “strange background” that encompasses physics, computer science, media technology, art, electrical engineering, and more. “That all comes together for me.” Indeed, you can find more in this companion piece on that background, which includes stints at Google X, One Laptop per Child (which she co-founded), the MIT Media Lab, Intel, her own startups, and more.

In the case of Open Water, part of her motivation is her own health. “I’m a brain tumor survivor,” she says. She had surgery to remove a brain tumor in 1995, and since then has taken pills “twice a day every day for the last 21 years to stay alive.” That has led her to read a lot on the side about neuroscience—and think about how to advance the field.

Part of the idea behind Open Water involves taking things at “the hairy edge of what physics can do,” Jepsen says, and then “using my substantial capability in consumer electronics” to make them possible at consumer electronics price points. She says there is a huge potential in the manufacturing plants in Asia that are primarily used to make OLEDs, LCDs, and such. Jepsen adds that these consumer electronics manufacturers have been mostly focused on smartphones for the past decade or so. But, she says, we’ve reached saturation in mobile phones, and sales are declining. “What I see,” she says, are “the subcomponent makers being really hungry for what the new, new thing is.”
“My big bet is we can use that manufacturing infrastructure to create the functionality of a $5 million MRI machine in a consumer electronics price-point wearable. And the implications of that are so big.” She says every doctor’s office in the world could afford these wearable devices and use them for early detection of neurodegenerative disease, cancer, cardiovascular disease, internal bleeding, blood clots, and more.

“It’s such a big idea, it’s what I wanted to do for a decade. It’s why I went to MIT [Media Lab]. It’s why I went to Google,” she says. “It turned out that Google really needed me to do some other stuff that was way more important to Google at the time. I’ve been incubating this since 2005…and I clearly see how to do it and how to realize it in a few short years.”

One factor in advancing her idea was work published about five years ago by a group led by Jack Gallant at U.C. Berkeley, Jepsen says. The research group used a functional magnetic resonance imaging scanner to track blood flow and oxygen flow and image the brains of people shown hundreds of hours of videos. You can read more about it here, but the main point Jepsen stressed to me was that the work (and subsequent work) has produced a library or database of sorts of how brains react to different images. A computer using artificial intelligence can then use such a database to basically look at MRI brain images in real time and interpret what people are thinking about or reacting to. This ability has been demonstrated at dozens of labs to gauge the brain’s reactions to words, music, math equations, and more, she says. But the resolution is poor and the process is expensive, requiring people to lie still in big chambers inside a huge magnet.

“I was really struck by that, so I started thinking this is great, but we need to up the resolution,” she says. “It’s in my head, I’ve got this plan. I’ve got these inventions that I’m working on, and my next step is to let myself pursue it full time.”

It is easy to see the power of these ideas to help make MRI far more affordable and accessible. But for Jepsen, that is just Phase One. She talks about the ability to image human thoughts in new ways, for instance, by helping stroke sufferers who can’t talk find a new way to communicate via their thoughts. Or for amputees to harness their thoughts to move prosthetics more naturally.
And then she goes a step or two farther. “Can you imagine a movie director waking up with an image of a new scene in her head, and just being able to dump her dream” into a computer, she says. ”It could be so much more efficient than the way we do it now.” For musicians, she muses, this could be “a way to get the music out of your head.”

But that’s not all. “Maybe we can communicate with animals, maybe we can scan animal brains and see what images they are thinking of,” Jepsen says. “So little is known. Dolphins are supposed to be really smart—maybe we can collaborate with them.”

It all sounds pretty far-out, I know, and she says so, too. But given how long Jepsen has had these ideas in her head—and how much work has been done in brain-machine interfaces—perhaps the world is finally ready to receive her thoughts.

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 http://gallantlab.org/index.php/publications/nishimoto-et-al-2011/




Reconstructing visual experiences from brain activity evoked by natural movies


Shinji Nishimoto, An T. Vu, Thomas Naselaris, Yuval Benjamini, Bin Yu & Jack L. Gallant (Current Biology 2011, PDF 1.4M).

Quantitative modeling of human brain activity can provide crucial insights about cortical representations and can form the basis for brain decoding devices. Recent functional magnetic resonance imaging (fMRI) studies have modeled brain activity elicited by static visual patterns and have reconstructed these patterns from brain activity. However, blood oxygen level-dependent (BOLD) signals measured via fMRI are very slow, so it has been difficult to model brain activity elicited by dynamic stimuli such as natural movies. Here we present a new motion-energy encoding model that largely overcomes this limitation. The model describes fast visual information and slow hemodynamics by separate components. We recorded BOLD signals in occipitotemporal visual cortex of human subjects who watched natural movies and fit the model separately to individual voxels. Visualization of the fit models reveals how early visual areas represent the information in movies. To demonstrate the power of our approach, we also constructed a Bayesian decoder by combining estimated encoding models with a sampled natural movie prior. The decoder provides remarkable reconstructions of the viewed movies. These results demonstrate that dynamic brain activity measured under naturalistic conditions can be decoded using current fMRI technology.





Frequently asked questions about this work

Could you give a simple outline of the experiment?

The goal of the experiment was to design a process for decoding dynamic natural visual experiences from human visual cortex. More specifically, we sought to use brain activity measurements to reconstruct natural movies seen by an observer. First, we used functional magnetic resonance imaging (fMRI) to measure brain activity in visual cortex as a person looked at several hours of movies. We then used these data to develop computational models that could predict the pattern of brain activity that would be elicited by any arbitrary movies (i.e., movies that were not in the initial set used to build the model). Next, we used fMRI to measure brain activity elicited by a second set of movies that were completely distinct from the first set. Finally, we used the computational models to process the elicited brain activity, in order to reconstruct the movies in the second set of movies. This is the first demonstration that dynamic natural visual experiences can be recovered from very slow brain activity recorded by fMRI.

Can you give an intuitive explanation of movie reconstruction?

As you move through the world or you watch a movie, a dynamic, ever-changing pattern of activity is evoked in the brain. The goal of movie reconstruction is to use the evoked activity to recreate the movie you observed. To do this, we create encoding models that describe how movies are transformed into brain activity, and then we use those models to decode brain activity and reconstruct the stimulus.

Can you explain the encoding model and how it was fit to the data?

To understand our encoding model, it is most useful to think of the process of perception as one of filtering the visual input in order to extract useful information. The human visual cortex consist of billions of neurons. Each neuron can be viewed as a filter that takes a visual stimulus as input, and produces a spiking response as output. In early visual cortex these neural filters are selective for simple features such as spatial position, motion direction and speed. Our motion-energy encoding model describes this filtering process. Currently the best method for measuring human brain activity is fMRI. However, fMRI does not measure neural activity directly, but rather measures hemodynamic changes (i.e. changes in blood flow, blood volume and blood oxygenation) that are caused by neural activity. These hemodynamic changes take place over seconds, so they are much slower than the changes that can occur in natural movies (or in the individual neurons that filter those movies). Thus, it has previously been thought impossible to decode dynamic information from brain activtiy recorded by fMRI. To overcome this fundamental limitation we use a two stage encoding model. The first stage consists of a large collection of motion-energy filters that span a range of positions, motion directions and speeds as the underlying neurons. This stage models the fast responses in the early visual system. The output from the first stage of the model is fed into a second stage that describes how neural activity affects hemodynamic activity in turn. The two stage processing allows us to model the relationship between the fine temporal information in the movies and the slow brain activity signals measured using fMRI. Functional MRI records brain activity from small volumes of brain tissue called voxels (here each voxel was 2.0 x 2.0 x 2.5 mm). Each voxel represents the pooled activity of hundreds of thousands of neurons. Therefore, we do not model each voxel as a single motion-energy filter, but rather as a bank of thousands of such filters. In practice fitting the encoding model to each voxel is a straightforward regression problem. First, each movie is processed by a bank of nonlinear motion-energy filters. Next, a set of weights is found that optimally map the filtered movie (now represented as a vector of about 6,000 filter outputs) into measured brain activity. (Linear summation is assumed in order to simplify fitting.)

How accurate is the decoder?

A good decoder should produce a reconstruction that a neutral observer judges to be visually similar to the viewed movie. However, it is difficult to quantify human judgments of visual similarity. In this paper we use similarity in the motion-energy domain. That is, we quantify how much of the spatially localized motion information in the viewed movie was reconstructed. The accuracy of our reconstructions is far above chance.

Other studies have attempted reconstruction before. How is your study different?

Previous studies showed that it is possible to reconstruct static visual patterns (Thirion et al., 2006 Neuroimage; Miyawaki et al., 2008 Neuron), static natural images (Naselaris et al., 2009 Neuron) or handwriting digits (van Gerven et al. 2010 Neural Computation). However, no previous study has produced reconstructions of dynamic natural movies. This is a critical step toward obtaining reconstructions of internal states such as imagery, dreams and so on.

Why is this finding important?

From a basic science perspective, our paper provides the first quantitative description of dynamic human brain activity during conditions simulating natural vision. This information will be important to vision scientists and other neuroscientists. Our study also represents another important step in the development of brain-reading technologies that could someday be useful to society. Previous brain-reading approaches could only decode static information. But most of our visual experience is dynamic, and these dynamics are often the most compelling aspect of visual experience. Our results will be crucial for developing brain-reading technologies that can decode dynamic experiences.

How many subjects did you run? Is there any chance that they could have cheated?

We ran three subjects for the experiments in this paper, all co-authors. There are several technical considerations that made it advantageous to use authors as subjects. It takes several hours to acquire sufficient data to build an accurate motion-energy encoding model for each subject, and naive subjects find it difficult to stay still and alert for this long. Authors are motivated to be good subjects, to their data are of high quality. These high quality data enabled us to build detailed and accurate models for each individual subject. There is no reason to think that the use of authors as subjects weakens the validity of the study. The experiment focuses solely on the early part of the visual system, and this part of the brain is not heavily modulated by intention or prior knowledge. The movies used to develop encoding models for each subject and those used for decoding were completely separate, and there no plausible way that a subject could have changed their own brain activity in order to improve decoding. Many fMRI studies use much larger groups of subjects, but they collect much less data on each subject. Such studies tend to average over a lot of the individual variability in the data, and the results provide a poor description of brain activity in any individual subject.

What are the limits on brain decoding?

Decoding performance depends on the quality of brain activity measurements. In this study we used functional MRI (fMRI) to measure brain activity. (Note that fMRI does not actually measure the activity of neurons. Instead, it measures blood flow consequent to neural activity. However, many studies have shown that the blood flow signals measured using fMRI are generally correlated with neural activity.) fMRI has relatively modest spatial and temporal resolution, so much of the information contained in the underlying neural activity is lost when using this technique. fMRI measurements are also quite variable from trial-to-trial. Both of these factors limit the amount of information that can be decoded from fMRI measurements. Decoding also depends critically on our understanding of how the brain represents information, because this will determine the quality of the computational model. If the encoding model is poor (i.e., if it does a poor job of prediction) then the decoder will be inaccurate. While our computational models of some cortical visual areas perform well, they do not perform well when used to decode activity in other parts of the brain. A better understanding of the processing that occurs in parts of the brain beyond visual cortex (e.g. parietal cortex, frontal cortex) will be required before it will be possible to decode other aspects of human experience.

What are the future applications of this technology?

This study was not motivated by a specific application, but was aimed at developing a computational model of brain activity evoked by dynamic natural movies. That said, there are many potential applications of devices that can decode brain activity. In addition to their value as a basic research tool, brain-reading devices could be used to aid in diagnosis of diseases (e.g., stroke, dementia); to assess the effects of therapeutic interventions (drug therapy, stem cell therapy); or as the computational heart of a neural prosthesis. They could also be used to build a brain-machine interface.

Could this be used to build a brain-machine interface (BMI)?

Decoding visual content is conceptually related to the work on neural-motor prostheses being undertaken in many laboratories. The main goal in the prosthetics work is to build a decoder that can be used to drive a prosthetic arm or other device from brain activity. Of course there are some significant differences between sensory and motor systems that impact the way that a BMI system would be implemented in the two systems. But ultimately, the statistical frameworks used for decoding in the sensory and motor domains are very similar. This suggests that a visual BMI might be feasible.

At some later date when the technology is developed further, will it be possible to decode dreams, memory, and visual imagery?

Neuroscientists generally assume that all mental processes have a concrete neurobiological basis. Under this assumption, as long as we have good measurements of brain activity and good computational models of the brain, it should be possible in principle to decode the visual content of mental processes like dreams, memory, and imagery. The computational encoding models in our study provide a functional account of brain activity evoked by natural movies. It is currently unknown whether processes like dreaming and imagination are realized in the brain in a way that is functionally similar to perception. If they are, then it should be possible to use the techniques developed in this paper to decode brain activity during dreaming or imagination.

At some later date when the technology is developed further, will it be possible to use this technology in detective work, court cases, trials, etc?

The potential use of this technology in the legal system is questionable. Many psychology studies have now demonstrated that eyewitness testimony is notoriously unreliable. Witnesses often have poor memory, but are usually unaware of this. Memory tends to be biased by intervening events, inadvertent coaching, and rehearsal (prior recall). Eyewitnesses often confabulate stories to make logical sense of events that they cannot recall well. These errors are thought to stem from several factors: poor initial storage of information in memory; changes to stored memories over time; and faulty recall. Any brain-reading device that aims to decode stored memories will inevitably be limited not only by the technology itself, but also by the quality the stored information. After all, an accurate read-out of a faulty memory only provides misleading information. Therefore, any future application of this technology in the legal system will have to be approached with extreme caution.

Will we be able to use this technology to insert images (or movies) directly into the brain?

Not in the foreseeable future. There is no known technology that could remotely send signals to the brain in a way that would be organized enough to elicit a meaningful visual image or thought.

Does this work fit into a larger program of research?

One of the central goals of our research program is to build computational models of the visual system that accurately predicts brain activity measured during natural vision. Predictive models are the gold standard of computational neuroscience and are critical for the long-term advancement of brain science and medicine. To build a computational model of some part of the visual system, we treat it as a “black box” that takes visual stimuli as input and generates brain activity as output. A model of the black box can be estimated using statistical tools drawn from classical and Bayesian statistics, and from machine learning. Note that this reverse-engineering approach is agnostic about the specific way that brain activity is measured. One good way to evaluate these encoding models is construct a corresponding decoding model, and then assess its performance in a specific task such as movie reconstruction.

Why is it important to construct computational models of the brain?

The brain is an extremely complex organ and many convergent approaches are required to obtain a full understanding of its structure and function. One way to think about the problem is to consider three different general goals of research in systems/computational neuroscience. (1) The first goal is to understand how the brain is divided into functionally distinct modules (e.g., for vision, memory, etc.). (2) The second goal, contingent on the first, is to determine the function of each module. One classical approach for investigating the function of a brain circuit is to characterize neural responses at a quantitative computational level that is abstracted away from many of the specific anatomical and biophysical details of the system. This helps make tractable a problem that would otherwise seem overwhelmingly complex. (3) The third goal, contingent on the first two, is to understand how these specific computations are implemented in neural circuitry. A byproduct of this model-based approach is that it has many specific applications, as described above.

Can you briefly explain the function of the parts of the brain examined here?

The human visual system consists of several dozen distinct cortical visual areas and sub-cortical nuclei, arranged in a network that is both hierarchical and parallel. Visual information comes into the eye and is there transduced into nerve impulses. These are sent on to the lateral geniculate nucleus and then to primary visual cortex (area V1). Area V1 is the largest single processing module in the human brain. Its function is to represent visual information in a very general form by decomposing visual stimuli into spatially localized elements. Signals leaving V1 are distributed to other visual areas, such as V2 and V3. Although the function of these higher visual areas is not fully understood, it is believed that they extract relatively more complicated information about a scene. For example, area V2 is thought to represent moderately complex features such as angles and curvature, while high-level areas are thought to represent very complex patterns such as faces. The encoding model used in our experiment was designed to describe the function of early visual areas such as V1 and V2, but was not meant to describe higher visual areas. As one might expect, the model does a good job of decoding information in early visual areas but it does not perform as well in higher areas.

Are there any ethical concerns with this type of research?

The current technology for decoding brain activity is relatively primitive. The computational models are immature, and in order to construct a model of someone’s visual system they must spend many hours in a large, stationary magnetic resonance scanner. For this reason it is unlikely that this technology could be used in practical applications any time soon. That said, both the technology for measuring brain activity and the computational models are improving continuously. It is possible that decoding brain activity could have serious ethical and privacy implications downstream in, say, the 30-year time frame. As an analogy, consider the current debates regarding availability of genetic information. Genetic sequencing is becoming cheaper by the year, and it will soon be possible for everyone to have their own genome sequenced. This raises many issues regarding privacy and the accessibility of individual genetic information. The authors believe strongly that no one should be subjected to any form of brain-reading process involuntarily, covertly, or without complete informed consent.