TEDGlobal 2010
Tan Le: A headset that reads your brainwaves
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Tan Le's astonishing new computer interface reads its user's brainwaves, making it possible to control virtual objects, and even physical electronics, with mere thoughts (and a little concentration). She demos the headset, and talks about its far-reaching applications.
Tan Le - Entrepreneur
Tan Le is the founder & CEO of Emotiv, a bioinformatics company that's working on identifying biomarkers for mental and other neurological conditions using electroencephalography (EEG). Full bio
Tan Le is the founder & CEO of Emotiv, a bioinformatics company that's working on identifying biomarkers for mental and other neurological conditions using electroencephalography (EEG). Full bio
Double-click the English transcript below to play the video.
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Up until now, our communication with machines
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has always been limited
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to conscious and direct forms.
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Whether it's something simple
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like turning on the lights with a switch,
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or even as complex as programming robotics,
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we have always had to give a command to a machine,
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or even a series of commands,
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in order for it to do something for us.
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Communication between people, on the other hand,
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is far more complex and a lot more interesting
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because we take into account
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so much more than what is explicitly expressed.
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We observe facial expressions, body language,
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and we can intuit feelings and emotions
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from our dialogue with one another.
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This actually forms a large part
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of our decision-making process.
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Our vision is to introduce
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this whole new realm of human interaction
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into human-computer interaction
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so that computers can understand
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not only what you direct it to do,
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but it can also respond
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to your facial expressions
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and emotional experiences.
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And what better way to do this
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than by interpreting the signals
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naturally produced by our brain,
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our center for control and experience.
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Well, it sounds like a pretty good idea,
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but this task, as Bruno mentioned,
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isn't an easy one for two main reasons:
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First, the detection algorithms.
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Our brain is made up of
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billions of active neurons,
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around 170,000 km
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of combined axon length.
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When these neurons interact,
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the chemical reaction emits an electrical impulse,
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which can be measured.
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The majority of our functional brain
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is distributed over
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the outer surface layer of the brain,
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and to increase the area that's available for mental capacity,
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the brain surface is highly folded.
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Now this cortical folding
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presents a significant challenge
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for interpreting surface electrical impulses.
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Each individual's cortex
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is folded differently,
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very much like a fingerprint.
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So even though a signal
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may come from the same functional part of the brain,
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by the time the structure has been folded,
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its physical location
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is very different between individuals,
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even identical twins.
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There is no longer any consistency
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in the surface signals.
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Our breakthrough was to create an algorithm
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that unfolds the cortex,
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so that we can map the signals
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closer to its source,
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and therefore making it capable of working across a mass population.
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The second challenge
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is the actual device for observing brainwaves.
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EEG measurements typically involve
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a hairnet with an array of sensors,
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like the one that you can see here in the photo.
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A technician will put the electrodes
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onto the scalp
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using a conductive gel or paste
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and usually after a procedure of preparing the scalp
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by light abrasion.
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Now this is quite time consuming
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and isn't the most comfortable process.
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And on top of that, these systems
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actually cost in the tens of thousands of dollars.
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So with that, I'd like to invite onstage
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Evan Grant, who is one of last year's speakers,
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who's kindly agreed
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to help me to demonstrate
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what we've been able to develop.
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(Applause)
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So the device that you see
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is a 14-channel, high-fidelity
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EEG acquisition system.
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It doesn't require any scalp preparation,
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no conductive gel or paste.
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It only takes a few minutes to put on
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and for the signals to settle.
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It's also wireless,
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so it gives you the freedom to move around.
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And compared to the tens of thousands of dollars
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for a traditional EEG system,
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this headset only costs
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a few hundred dollars.
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Now on to the detection algorithms.
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So facial expressions --
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as I mentioned before in emotional experiences --
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are actually designed to work out of the box
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with some sensitivity adjustments
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available for personalization.
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But with the limited time we have available,
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I'd like to show you the cognitive suite,
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which is the ability for you
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to basically move virtual objects with your mind.
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Now, Evan is new to this system,
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so what we have to do first
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is create a new profile for him.
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He's obviously not Joanne -- so we'll "add user."
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Evan. Okay.
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So the first thing we need to do with the cognitive suite
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is to start with training
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a neutral signal.
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With neutral, there's nothing in particular
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that Evan needs to do.
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He just hangs out. He's relaxed.
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And the idea is to establish a baseline
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or normal state for his brain,
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because every brain is different.
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It takes eight seconds to do this,
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and now that that's done,
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we can choose a movement-based action.
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So Evan, choose something
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that you can visualize clearly in your mind.
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Evan Grant: Let's do "pull."
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Tan Le: Okay, so let's choose "pull."
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So the idea here now
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is that Evan needs to
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imagine the object coming forward
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into the screen,
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and there's a progress bar that will scroll across the screen
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while he's doing that.
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The first time, nothing will happen,
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because the system has no idea how he thinks about "pull."
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But maintain that thought
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for the entire duration of the eight seconds.
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So: one, two, three, go.
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Okay.
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So once we accept this,
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the cube is live.
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So let's see if Evan
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can actually try and imagine pulling.
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Ah, good job!
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(Applause)
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That's really amazing.
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(Applause)
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So we have a little bit of time available,
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so I'm going to ask Evan
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to do a really difficult task.
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And this one is difficult
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because it's all about being able to visualize something
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that doesn't exist in our physical world.
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This is "disappear."
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So what you want to do -- at least with movement-based actions,
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we do that all the time, so you can visualize it.
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But with "disappear," there's really no analogies --
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so Evan, what you want to do here
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is to imagine the cube slowly fading out, okay.
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Same sort of drill. So: one, two, three, go.
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Okay. Let's try that.
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Oh, my goodness. He's just too good.
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Let's try that again.
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EG: Losing concentration.
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(Laughter)
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TL: But we can see that it actually works,
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even though you can only hold it
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for a little bit of time.
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As I said, it's a very difficult process
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to imagine this.
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And the great thing about it is that
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we've only given the software one instance
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of how he thinks about "disappear."
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As there is a machine learning algorithm in this --
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(Applause)
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Thank you.
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Good job. Good job.
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(Applause)
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Thank you, Evan, you're a wonderful, wonderful
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example of the technology.
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So, as you can see, before,
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there is a leveling system built into this software
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so that as Evan, or any user,
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becomes more familiar with the system,
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they can continue to add more and more detections,
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so that the system begins to differentiate
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between different distinct thoughts.
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And once you've trained up the detections,
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these thoughts can be assigned or mapped
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to any computing platform,
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application or device.
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So I'd like to show you a few examples,
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because there are many possible applications
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for this new interface.
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In games and virtual worlds, for example,
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your facial expressions
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can naturally and intuitively be used
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to control an avatar or virtual character.
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Obviously, you can experience the fantasy of magic
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and control the world with your mind.
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And also, colors, lighting,
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sound and effects
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can dynamically respond to your emotional state
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to heighten the experience that you're having, in real time.
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And moving on to some applications
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developed by developers and researchers around the world,
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with robots and simple machines, for example --
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in this case, flying a toy helicopter
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simply by thinking "lift" with your mind.
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The technology can also be applied
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to real world applications --
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in this example, a smart home.
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You know, from the user interface of the control system
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to opening curtains
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or closing curtains.
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And of course, also to the lighting --
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turning them on
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or off.
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And finally,
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to real life-changing applications,
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such as being able to control an electric wheelchair.
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In this example,
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facial expressions are mapped to the movement commands.
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Man: Now blink right to go right.
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Now blink left to turn back left.
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Now smile to go straight.
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TL: We really -- Thank you.
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(Applause)
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We are really only scratching the surface of what is possible today,
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and with the community's input,
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and also with the involvement of developers
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and researchers from around the world,
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we hope that you can help us to shape
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where the technology goes from here. Thank you so much.
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ABOUT THE SPEAKER
Tan Le - EntrepreneurTan Le is the founder & CEO of Emotiv, a bioinformatics company that's working on identifying biomarkers for mental and other neurological conditions using electroencephalography (EEG).
Why you should listen
Tan Le is the co-founder and president of Emotiv. Before this, she headed a firm that worked on a new form of remote control that uses brainwaves to control digital devices and digital media. It's long been a dream to bypass the mechanical (mouse, keyboard, clicker) and have our digital devices respond directly to what we think. Emotiv's EPOC headset uses 16 sensors to listen to activity across the entire brain. Software "learns" what each user's brain activity looks like when one, for instance, imagines a left turn or a jump.
Le herself has an extraordinary story -- a refugee from Vietnam at age 4, she entered college at 16 and has since become a vital young leader in her home country of Australia.
Tan Le | Speaker | TED.com