Name: Web Audio APIで脳波を解読する by Braden Moore｜JSConf Budapest 2019 (Deciphering Brainwaves with the Web Audio API by Braden Moore | JSConf Budapest 2019)
Uploaded: 2021-01-14T10:34:10.000Z
Duration: 27 min 23 s
Description: VoiceTubeの動画で発音を聞きながら英語表現を覚えよう！学べる英語：

程度の副詞

My colleagues not is something pretty exciting.

様態の副詞

Using only the brows, a little bit of JavaScript and something called the Web audio a p I we were able to decipher brainwaves.

Now that sounds little, pretty little bit sensational, but it's it's mostly true.

So today I'd like to tell you the story of our journey through the lows and highs of Web audio on requested a cipher Brain waves in the browser.

Discovery is about finding out what the Web can offer.

It's a story of exploration about pushing the boundaries of Web technologies.

Thio benefit to make a difference, and last, it's Well, it's a story has a really cool demo at the end.

But before I can tell you that story, I have to tell you this one.

I'm a full stack developer at an Australian medical technology startup called CIA so CIA provide destroyers, largest epilepsy management and diagnostic service.

In fact, the tagline of our corporate website is if lipstick diagnosis made easy now, I didn't know anything about epilepsy before I started working at CIA on and I guess that's kind of true for a lot of people.

So epilepsy is a chronic condition that effects over six million people worldwide.

Now the symptoms of that let's skin vary from person to person but could range from boss awareness, loss of bodily control to full body seizures.

Now these events can vary in frequency, sometimes having multiple times a day after once a month or even this is rails once a year.

But the results can be devastating regardless, and there's no known cure for all types of epilepsy.

Some of the symptoms can be treated, so the way that it flexes diagnosed is by reviewing a patient's brain waves in what's called an e e g or electroencephalogram.

So each of these traces they represent the electoral signals from certain parts of the patient's brain captured using electrodes on the head.

Just like these to help provide a diagnosis At the service that we offered to see, looks like this patient comes to clinics were to get connected with his electrodes to monitor their brain waves over the next seven days.

They can live their life in the comfort of their own home while being monitored.

Then, after that, time to come back to our clinic to get disconnected.

And then we spend a few weeks looking through that data and looking for something that might give us a diagnosis.

What is it that the scientists are looking for?

Well, look, sometimes looking for the obvious.

They're looking for something like these minute long sieges that are pretty easy to capture on tape.

But if these are very rare events happening once a month or even once a year, it's not guarantee that will capture that during the seven days getting wanted it so more often they're looking for something a bit smaller.

They're looking for something like a needle in the haystack.

Well, in epilepsy, there's something called the characteristic signal of epilepsy, which is called a spike way.

These little spikes and little waves, um, that around half a second long each and so, while a patient might only have a seizure once a month, they might have dozens of these little spike waves every day.

And so it's these needle in a haystack said that scientists looking for In fact, you're looking at a whole kind of run.

But what does a Web developer I have to do with it?

I mentioned before that our clinical team gets this daughter and then they start reviewing it.

Like every second company these days, we have our own cloud platform, and it's a platform for secure management stories and review off medical data.

So one of the powerful things about this platform is that allows a clinical team to upload this E e g brainwave data and then review it anywhere at any time or through a browser.

One of the major technical challenges off this building is platform is the ability to view all they start on the platform.

So to tackle this problem, we've built something that's very creatively called out viewer to view the data, the name because it's hot and it looks something like this.

This is a screen shot I just took in a few minutes ago.

And so it's this dot of you that are clinical team uses to review these seven days worth of data and to label events and to eventually deliver a diagnosis.

One of the big challenges in getting US scientists to move from traditional one of the big challenges that we face since Platform is getting a neuro physiologist, as they called to move from traditional desktop based software into a cloud platform.

Now, uh, they often neurophysiologist often flick through Edie, not a very quickly.

So flick, flick flick that is looking very quickly through this because if you want to get through lots of data, you don't be sending a lot of time on each page.

The way they're able to accomplish that is because they're trained to look for patterns.

They're looking for certain things like the spike and waves.

Remember, that's 1/2 2nd thing in a 12th page that you're looking at for these pages per second.

So you're looking for a needle in a haystack.

What happens when a needle doesn't look like a needle?

E g data you're trained to look for doesn't look quite right.

Well, in cases like those that was could lead to misdiagnosis and awful patient outcomes.

How can we take E E t review from desktop to the browser?

So there's one particular aspect off displaying this e e g.

That's very important, but also a little bit tricky, and that's called signal filtering.

So what's this problem of signal filtering that we're going to explore today?

Let's illustrate the problem on the left us an e e g data that has signal field is applied.

On the right is that same daughter with filters turned off.

So clearly there's a very big difference between these two.

It's still, you know, recording the brain here.

Well, like most electoral signals, when you're recording something, you're not just a recording that brain activity.

You're also recording a bunch of extra noise and artifact so some of this noise can come from.

Contractions could come from like DC drift, or it can come from like the mains electricity noise.

So all of these things aren't brain waves, but they still shoppin this E e g.

Let me explain what a filter is by talking a bit about music.

It's a visual representation off all the frequencies off a piece of audio, like your favorite song at a given point in time.

So it has, like low frequency is like the bass and drums, mid frequencies like another spoken voice and maybe higher frequencies like wind chimes or pan flutes or other super common instruments that you hear.

So, just like audio, your brain wave is also composed off.

A range of different frequencies Fall happening at the same time.

Imagine you're producing a song, but you've got this kind of bass noise you don't really want in the air or there's too much pan flute.

Well, one way to get rid of that is by filtering out like the lower frequency or filtering out the higher frequencies.

G is kind of three main filters we'd like to apply.

You can have a high pass filter which is like this, unless everything higher than the filter frequency passed through.

There's a notch filter, which notches or cuts out the theme free range of frequencies and a low pass, which is the opposite of your high pass filter.

Everything lower than the field Africans he passes through.

You can have multiple of these filters running at once, and that's exactly what we'd like to do.

So okay, how can we go about implementing filters and JavaScript in the browser on the cloud?

字幕リスト動画再生

Web Audio APIで脳波を解読する by Braden Moore｜JSConf Budapest 2019 (Deciphering Brainwaves with the Web Audio API by Braden Moore | JSConf Budapest 2019)

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