This week our guest is Tan Le, who is CEO of the bioinformatics company Emotiv, which makes wearable EEG neuro-headsets that enable people to collect data on their brain activity. Tan Le has a truly remarkable story, from Vietnamese refugee, to studying Law, to becoming the Young Australian of the Year, all the way to her current role helping to drive the accessibility of neuroscience.
In this episode, we explore her journey, including how someone without neuroscience training can nonetheless create a company that pushes the field forward, and we also dig more deeply into how the headsets work and the future Tan believes we’ll create with such technology.
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Music by: Amine el Filali
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Tan Le, Steven Parton
Tan Le 00:00
We can connect humans and their brains to the world around us. And so that it becomes a really an environment becomes an extension of your brain and we no longer separate entities anymore.
Steven Parton 00:27
Hello, everyone, my name is Steven Parton. And you're listening to the feedback loop on singularity radio, where we keep you up to date on the latest technological trends, and how they're impacting the transformation of consciousness and culture from the individual to society at large. This week, our guest is Tom Lee, who is the CEO of the bioinformatics company, Emotiv who is well known for making wearable eg headsets that enable people to collect data on their brain activity. Tom Lee has a truly remarkable story from Vietnamese refugee to studying law to becoming Australia's young person of the year, all the way to her current role, where she is helping to make neuroscience more accessible. In this episode, we explore her fascinating journey, including how someone without neuroscience training can nonetheless create a company that pushes the field forward. And we also dig more deeply into how the headsets work, and what kind of future Tom believes that we'll be able to create with this technology. As usual, before we start, I do want to quickly remind you that we are still offering two free weeks of premium membership at singularity for podcast listeners. And this includes regular access to workshops, networking events, white papers, and a whole bunch more. So if this is something you're interested in, please be sure to check the show description for a link that will let you start your two week trial. Now with that out of the way, let's go ahead and get into it. Everyone, please welcome to the feedback loop. Tom Lee. Where I would love to start at least is to hear more about your journey from Vietnam, to law, and then into neuroscience, how do you make that progression in your life?
Tan Le 02:15
I it's an interesting progression, isn't it? So I, the first one, I had really no choice about my after the end of the Vietnam War things in Vietnam are pretty tough for most people there and my family, obviously was no exception. So my mother decided, with two young girls, and the future, our future ahead of her, she wanted to make sure that we would have some choice over what we did with our life. And there would be possibilities available to us. And so she decided to make a very difficult decision to leave behind her roots and her history and to branch out until a little boat and try and make that escape. And we were very lucky because after five days and nights at sea, we were rescued just off the coast of Malaysia in the South China Sea by a British oil rig and the men there, took us on board and sent us on our merry way to the refugee camp in Malaysia, which was where we stayed for a few months before Australia accepted us and gave us a new home. And so that's where my my family restarted our life. And I grew up in Australia. And so that's the exit. And your law was really a choice that I've made as a 16 year old so I finish high school relatively young. And at that point in my life when I was trying to decide what was the right choice for me in terms of a career path, I felt that given my family's background, and the community that I was a part of being a part of a non English speaking background, and a community of new migrants to a new country that we now called home. It made sense for us to really understand the social infrastructure. What made this country tick, what are the policies, what are the policy considerations and law provides a framework for how you engage as citizens and I felt it was important for someone in my position to understand that and muster my mother's disappointment because she wanted me to pursue medicine just like any other Asian parent. Because you know, with medicine, you actually help people you save people, you save lives, and that's very important. And law she thought was very valuable as well, but she thought that I would make a more meaningful contribution if I pursued medicine anyway, I wasn't the right choice for me, I decided and so I chose more. And after starting my career in law at a large commercial law firms, I finished my degree Got into a really large law firm in Australia really loved the work. It was very, very intellectually stimulating. But I, what I quickly realized when I was practicing law was that we were somehow I missed the memo about this digital age, this information age that was driving change, not only in my own community, but in the, in the world in the global context, the one thing that was going to make the true impact in our lives in this lifetime, at least at this juncture in humanity's history is the technological revolution. And I didn't want to sit on the sidelines, facilitating all of that, as a lawyer, I wanted to be a part of that creation, I wanted to be part of the innovation process, I wanted to create new things, I want to invent something that hadn't been invented before. And so it was very obvious to me at that point that I wanted to get out of law and pursue something of my own, and to create something of my own. And after one first venture in in tech, I decided that I was ready to look for something that I could really invest most of my life to, because by that stage, I had been reinventing myself every five to six years, and pursuing new categories, huge areas of endeavors. And I realized that things that really are meaningful to you, you can spend your whole life pursuing. And that was what I was hoping, I would find. And so I chose something that was very green fields, that had a lot of open space to pursue. That was really intrinsically very interesting, not only to me, but to a lot of people. Because the brain essentially defines your perception, your experience of the world, it defines who you are. And yet we know so little about it. And so it was the perfect, intellectually challenging combination technologically interesting. There was a lot of, there was a lot to pursue and to explore. And so it's now my love, and it has been my love. I know. So it was definitely a challenging enough field that I could spend a lot of time working on and not have to get bored or feel that I would be tempted to move into other spaces. And so yeah, it's definitely captivated my attention for a very long time.
Steven Parton 07:35
And then, so that led you to a motive, right?
Tan Le 07:39
That's correct. That's, that led me to founding emotive and, you know, emotive is all about trying to change how, when, and why we study brains, because when I first started, because I wasn't a neuroscientist by training, one of the very first challenges that I observed when I first started to look into the area of neuroscience is the fact that we only study brains, and something's wrong with it, we typically only look at brains, when you have an accident and have a traumatic brain injury, or you have trouble sleeping, or you have something something's not quite right, with the way that your brain functions in order for us to study the brain. And yet, we 7 billion people on this planet, each of us has a unique brain, we need to find new ways of actually measuring the brain in context, because the context actually matters in this situation. And so my passion is to find, at least when we started a motive was to find new ways of being able to measure and image what's going on in the brain. In all of the diverse scenarios where we can, where the brain is in action, which means that it can be it can be at any point in time and anywhere. And so the goal was to create technology that was not only revolutionary at the time, because at the time when I first started this space, Eg scanning equipment was very expensive. And so we were the very first to pioneer technology that was 100 times more affordable than the systems that existed before. Now, you know, this is many years since then, and so it doesn't seem very new anymore, but at the time, it was a real breakthrough to be able to, you know, take this piece of equipment that was traditionally relegated to the lads to the clinics to hospitals. And to make it easy enough to use and pick up by a lay person, allow them to such fit the equipment on their head, and then to start seeing real time brain activity with very, very little prep time. So you we talk something that would take 45 minutes to an hour with a dedicated technician to put on your head so we use electroencephalography which has existed since the 1930s to observe brains for long periods of time, so we knew the technology was safe to use in that context, it's only measuring the electrical fluctuations that result from neurons firing and interacting with each other. And so that electrical pattern is emitted all the time. It's not as if we're sending signals back into the brain that was relatively safe. And we felt that if we could take that piece of equipment, make it easy enough to use take the setup time from 45 minutes to an hour with a dedicated technician to something that anyone can just put on in a few minutes. And then not only that, can you you can actually see real time brain activity in context of these, this equipment is wireless, and then slashed the cost by 100 times you could really, it could be game changing. And that's what we pursued initially. So changing the game for neuroscience research, because this is fundamentally what we want to innovate, what we want to change level, the playing field allow as many people as possible to have access to this equipment so that they can start measuring, observing what goes on in the brain. And then from there build really interesting machine learning algorithms, because we can collect data at scale. So one of the one of the big challenges in the past is because you only study brains, when something's wrong, you have a lot of blips of inflammation, so to speak, you have moments in time where you might have a burst of information about an individual's brain, but then there are long gaps between the next point. And you also don't have any idea of how the brain changes when it's actually healthy. And so it's hard to build machine learning algorithms that can predict changes in the brain. And a lot of conditions that, that people suffer from neurodevelopmental disorders or neurodegenerative conditions, which means that they develop over a period of time you don't, they don't happen. Due to an occurrence of some major activity, like a brain, like an accident, like a traumatic event that causes the brain to be damaged, a lot of the conditions happen over a long period of time. And so being able to identify and see how the brain looks in a healthy state, and how it progresses over time, in order to predict much earlier on when it actually when the progression starts to develop in a healthy brain is really important, especially when you look at the global momentum around aging. So we are making so much progress in so many other fields that we know that human life is going to be extended to a very unnatural age, in many ways, from a biological standpoint, we are going to continue to evolve this space. And so if we keep living longer lives, if we can extend and improve cardiovascular disease, that we can treat a lot of the conditions that cause us today, or die from heart failure, then the big elephant in the room is going to be the brain that's once you get to 90 100 years old, a lot of the conditions that will kind of cause us to not enjoy the quality of life that we hope to have in our, you know, third, maybe fourth act, what are we gonna call it at that point in time, it's really going to be all about the brain. And so we need to tackle this. And so that's that's the whole notion around a mode of the vision, the mission that's been driving us for, for the last 10 years.
Steven Parton 13:35
And so to rewind a little bit there, how do you jump into something without neuroscience training and take something that is normally hundreds of 1000s of dollars and say, I'm gonna now come in and try to make it a few $100 or $1,000? Like, how do you even begin to tackle reducing the cost that much, and making something that much more accessible when it doesn't exist currently?
Tan Le 14:01
I think when you don't know enough, and you're a real outsider, I think the fact that you don't know how challenging it is, empowers you in in many ways to try new things that people who are domain experts will feel that it's not worth the effort because they know better in many ways. They know how hard it is, they know how challenging it is. And in some respects, that can be a limiting factor as well. So not knowing how challenging the problem set is, in some ways, can allow you to take more bold and audacious goals, but at the same time, at the end of the day, it's about it's not really just about having the audacity to do something. It really boils down to assembling a strong enough team that can keep at the problem set and breaking it down to its core elements at each part of the way and so you know, the first thing that we tackled, at least when, when I found that the company was to first get some comfort that there was some inflammation embedded in the electrical signals themselves. I think that, for me was the first challenge. Because if, if you can't really get anything valuable out of eg data in the first instance, there's really no point in democratizing the underlying technology in the same ways, if you look at an ECG, if it doesn't really have valuable information from from that, that from kind of really, really building more sophisticated models from there, and there's no point in democratizing that technology so that everyone can have access to it. But the nice thing about eg, for us was that when we started to build machine learning models, we found that we wish the algorithms could pick out very, very complex models that the human eye couldn't observe. And so we could see that at scale, this could be very, very interesting. And so from that, we felt, okay, this emboldened us to then say, yes, if there is a mark that enough information embedded in the electrical signals themselves, in the dynamic patterns that's emitted from, from what we can observe from the surface of the scalp, because at the end of the day, you're looking at a very noisy signal that's been attenuated, you know that the activity happens in the neurons firing itself, the patterns get, you transfer music through the cortex, and through the skull, which is very, very thick, through a layer the pair as well, and to onto the scalp surface. And so you're not getting the signal as if you were digging a probe directly into the, into the, into the brain. And so we knew that there were a lot of limitations to what this technology could do. We wanted to know, if you could just look at these patterns at that scale, could we find something interesting enough, so it's a bit like, if you look at a city scape, the topology of the city is like being able to build a map of where the, where the stadium is, where the schools are, where the CBD is, or the business district is where, where the homes and residential areas are. And so that's really quite if you have an fMRI, you can really look at structures of the brain so you can see where, where these things actually happen. But the nice thing about eg is that you can start to see the movement, so you can't see one person, right, so a singular cell, like moving across the network, there's no way you could see that with EG but you can see patterns. So you could see that people move, let's say, for example, just to keep the analog going, the you know, people not going from their homes in the residential area to school, and then going back at certain times of the day, they can, we can see that on the weekends, I might go to the stadium and observe a game. So those types of very large coordinated patterns you can see in the dynamics of the system. And that alone was enough to allow us to observe something that was really interesting about the dynamics of how the brain works, that was lost in some respects, when you're only looking at the structural elements, and so we felt that if you could look at this at scale, you could start to see whether the brain slowing down with it, the dynamics are starting to change. And that was really interesting information. So once we felt that, yes, the machine learning algorithm patterns that we can build from the observing these electrical changes were interesting and contained enough information, then it made sense to then tackle the engineering problem, which was the engineering challenge, which is to make this system more affordable address, the mechanical design issues address the electrical impedance challenges of trying to get the signal to noise ratio in the right. In an acceptable level, of course, with any sort of engineering challenge, it's about a cost optimization curve, there's no way you can get a system that is a few $100 to be absolutely equivalent to a $60,000 machine. But if you know the scope of what it is that you're wanting to achieve from this system, the types of signals you're trying to ascertain from the system, you can optimize the system to achieve that level of sophistication for that very specific subset of requirements that you have. So yes, if you want to look at a clinical system, you have time to have a technician put in the electrical gel abrasive skin and really put the the gel cap on of course you're going to get really great signals and you have the person sitting there is still as you're observing the signals, you're going to get you know, really Nice holiday clean eg data. But if you don't have that, that your if your application doesn't really need that, and you're not looking for that you want to be able to sell at scale, you want a lot of people a lot of data at the same time, then our system is really in terms of the cost optimization, because it's really optimal for that. And so, of course, with any engineering endeavor there, there are trade offs. And so that's the trade off that we want, we wanted something that was affordable, affordable enough, easy enough to set up that still preserve the characteristics of the signals that we were looking for, that would enable research at scale. But of course, we know that, you know, it's not a like for like comparison. That would be that would be impossible to really achieve, you know, to, in all fairness. And so it's not a intended to be a replacement of clinical systems, it's intended to be a way to observe the brain in context much more efficiently. And at a scale that you wouldn't be able to achieve if you were just to pursue a conventional model of looking at the brain. And how
Steven Parton 21:12
is the reliability on the eg data, because I know Recently, there has been some concern about fMRI data not being able to live up to replication. So they would have people come in do fMRI that did the same test later and realize that there's contradictory information there does eg run into that same issue at all,
Tan Le 21:30
I think the replica, the science reproducibility challenge, a lot of it has to do with a lack of standardized data, standardization in the methods, but also in the statistical relevance of the sample, because fMRI is quite expensive, and brains are so unique, it's really, really difficult to get a large enough scale, that you can just take any random individual in, and then be able to absolutely reproduce it. And so that's the, that's what we're trying to achieve with EG is to address this reproducibility or replicant ability challenge that exists in science in how we conduct science research, because if we keep, we did a, we did a review of 1000s of scientific publications, and even looking specifically at Ag and unfortunately, for the bulk of these papers, the sample sizes up 30 people or less. And so when you're looking at those sample sizes, I have, it's not a surprise to me, that we have this challenge in terms of reproducibility, that if we if we're able to move the sample sizes, and this is why we want to address scale, if we can change the paradigm in terms of how we're actually studying brains, and we're looking at sample sizes of 500 1000 people plus the robustness, and the reliability of the findings will be a lot better. Because at the end of the day, if you are looking for a feature set within a very, very small subset of the population, it's not going to be distinguishable, unfortunately. And so there are going to be there will be that situation and unless you've got to get to a large enough data set. And so that's why cost is so important. That's why scale and distribution and that Why think one of the things that we also found with EG studies is that with 7 billion brains on the planet, the environment actually makes a huge difference. And so currently, besides we have the, what is called the weird problems. So you know, Western, educated, industrialized, you know, basically, it's the student population. And so when you have this democratic countries, you've got this representative government, you've got this, basically Australia, UK, the United States driving most 80% of the published papers out there, it's not representative of the population of the global population. It is not, it is not representative of a diverse sample that you would need to have in order for your studies to be truly reproducible. And so one of the things that we really pride ourselves on at emoto is that we can help to address this challenge. Because we have a population in 120 plus countries already being able to deploy science experiments neuroscience experiment at scale to this global population, allows neuroscience researchers to address this bias that we have, and we can build very sophisticated machine learning models. But if the models are built on data that is biased intrinsically, then no matter how sophisticated your model and how accurate your model, it's still going to have that intrinsic bias in them and so correcting and building for that From the Ground Up is really important. So that's something that we have been very cognizant of in terms of how we've built our platform to ensure that if people want to address this challenge of, of reproducibility of being able to address this, not only the statistical significance, but also the bias, that's innate in how we actually sample how we build the participant population, this is something that we're addressing through our research platform. So yeah, it's a really exciting frontier to think about the science of the future being a lot more thoughtful, a lot more representative, and addresses some of the concerns that has existed for decades, because these sort of global platforms suddenly existed in the past.
Steven Parton 25:48
Yeah. And what kind of data are we really getting? You mentioned some great analogies before about the city and the activity between neurons. But for the average person, maybe the researcher, maybe the just hobbyist or the enterprise person who's using this technology, they get this headset what what do they then see what is the kind of information they can start to immediately track?
Tan Le 26:14
Yes, so that we have different tiers, depending on the nature of the user, we really focus on the neuroscience researcher, a large part of our focus is that community. And so the the New York for the neuroscience researcher, you get access to quality, Eg data that's been benchmarked against a $60,000 neuro scan system, which is a clinical device that's used for to study eg, and it's been benchmarked against a very difficult signal to find, which is a an audio response rate. So it's a it's an oddball response. So we know that the system is sufficiently sensitive to find some very, very difficult to identify signals. And so knowing that it's been benchmarked against that system to find that very tiny signal in the brain does have a lot of confidence that a neuroscience researcher taking the eg system, as long as you're able to get a clean eg signal, right, so we have a, it was essentially just a software program that provides that feedback loop so that you can see when your signals are in an optimal state, so it's actually clean, it doesn't have a lot of noise, you're able to achieve that, then you can get very, very high quality, Eg readings that will allow you to conduct any form of traditional neuroscience experiments where it's an AIP, whether it's your any sort of traditional type of eg study, then we've got the participant or the enthusiast market, where people are just interested in tinkering with the brain. In that case, we offer not only just the raw data, which is what neuroscientists use, we also offer what they can see their eg data, but they were not really allowing them to do the research on you could see it. But then we also provide the transformed data. So you can look at frequency domain, you can actually use a machine learning algorithms for things like mental commands, if you want to connect your mental commands to control a smart home or to move a little toy object around, you can start to tinker with that and explore that. Or if you just want to see your brain in real time, how it's changing dynamically, you can start to see that we have tools that allow you to see the visualization of what's happening in your brain in real time. And so from the enthusiast standpoint, there's a lot that you can do without actually being able to do hardcore neuroscience research. But I have to say that for them, the majority, our focus has been targeted at the neuroscience hardcore neuroscience research community because we believe that that really empowers the long term growth of this this field without impairing the neuroscience researcher, it's really difficult to create meaningful applications for consumer down the consumers down the line, because I do think it will take some time before we can deploy this sort of technology at scale to consumers, although emotive is, is starting to look into that space. At the moment, we've been working with some enterprise customers for the last two years on creating solutions that really help people optimize and achieve a balanced with their brain. So as we know, the human brain is not a machine, we can't work endlessly and especially with COVID. We know this we need. We need this focus time to do deep work to do that the work that we need to do as humans, but we also need to have great time in order to allow brains to restore and to replenish itself for the network's to to be replenished. And unfortunately, with this very long work day, and without enough space, it can be very difficult for us to find that balance. And so we are introducing some tools that will allow people to get that insight into their brain. And unfortunately, we can't do with the brain, what we can do with our weight, for example, help on a on a scale, and no, oh, I've put on a pound, let's not eat that extra piece of cake tonight or have that glass of wine. But unfortunately, with the grain, it's hard to know how it's doing, unless you have tools that actually tell you, because it's such a sophisticated learning machine, it will get good at what you put out it. And unfortunately, it will be harder and harder to realize that I'm actually super stressed, hyper stressful with five working all the time. But it's nice to have the tool that can tell you that, hey, you need a break. And also this type of break is best for you. So it will learn over time. And it'll be able to provide that feedback back. And so we're starting to think about what kind of tools we can introduce to a mass consumer audience that can help them start to build that relationship with their brain. And over time, we hope that this neuroscience community of researchers globally can start to think of build more and more insights about what can they learn about the brain? How do we find these early predictive markers, and those things can slowly be integrated into the platform down the line for consumers as well, once you've got a platform where you've got a lot of consumers using eg equipment regularly to just go about their normal day, you can then start to introduce more and more interesting applications that the neuroscience community can kind of enable, which is really exciting for us. That's really the long tail. And so we've really focused on supporting that neuroscience community. Because I think that's, that's what's going to make the difference in the long term.
Steven Parton 32:02
So is this something that I could put on at night in the morning when I wake up and start my day where until 9pm, go about a regular workday for a week and then come back and
Tan Le 32:13
see Oh, yeah, so so these are the, this is the new form factor, right? So it's actually even more skeletal than, than what you're wearing now. And it's quite nice, actually, because it's, it's in the form of the pair of earbuds, it doesn't make you look like you're actually wearing a brain scanning device, which is great. It has basic audio and microphone integrated as well. So you can conduct a normal day, you can have the conversations you can, you can take your calls, but at the same time, it's actually looking at your brain and providing the neat feedback for you. So you'll know, oh, I my optimal thing is to do three podcast recordings. And then I need to have a break. Or it might be that, you know, you need to do it in the in the mornings and take a break in the afternoon, or actually, you're an afternoon kind of person, and you should do yours in the evenings, it will actually provide that feedback to you and give you those windows, which is really interesting, because what we've learned from from doing these studies over the last two years is that humans are really unique. And unfortunately, this nine to five traditional workday that we've adopted as part of the Industrial Revolution doesn't really work. For most people, we've kind of, of course, we're humans. And so we're very adaptable. And so we've made it work for the majority. But now that we have the potential to create and support more flexible work day work, places work environments, I think it's a really interesting opportunity to think about how do you nurture the individual in the context of this larger environment? And that can we create much more dynamic, much more custom, much more individualized experiences that really put the human at the center of the experience rather than putting the system at the center? And then the humans responding to that, right. So I think it's a really interesting opportunity to create tech that will allow us to have better customization better individualization personalization over time. And, yeah, that's going to be something that we'll be able to watch and observe in the next few years to see how that technology starts to evolve, because we're starting to see that already, just not in the brain, sort of brain tech, neuro tech space, but I do think that we're starting to see elements of these themes that emerge in how people are crafting spaces, even in the public psyche, about this whole return to work, what the future of work looks like. And so I think it's a really exciting frontier where you can bring all of these big themes, these big concepts where people are thinking about It's already in the public psyche. It's in the governance A lot of organizations to be more thoughtful about recognizing the individual then bringing in tools that really allow you to unlock that potential in a meaningful way, by not just saying, You know what, I want to do this. But I'm going to do it with surveys, I'm going to do this, but I don't really have a way to really quantify how it's affecting my workforces. A lot of really interesting technology, I think that it's going to emerge in the next few years that will allow us to create that very, very good sort of virtuous, human involute type processes that didn't really exist in the past. So yeah, I think this is a technology that could be quite game changing in that field as well.
Steven Parton 35:42
Yeah, I love that idea of building the systems around the optimized human, rather than building a system that tries to optimize humans. And I, it's me thinking about having someone wear that headset, during a commute, and then go into work and see how they perform versus someone who does maybe stay at home and doesn't have the stress of a commute. I can see a lot of differences there.
Tan Le 36:04
I agree with you totally Stephen. And I actually think it's really interesting that we, you know, COVID has obviously catalyzed a lot of these changes. But a lot of organizations are still trying to understand what does it mean for us? How do we embrace the new normal? What does that look like? And so having tools that allow you to really say, wow, I actually do see the big impact on this, this person, right, I'm 60 70% of my employee cohort that is real, that there is a very measurable change in their efficiency, their productivity, their reduction in stress, it empowers us to emboldened us to do things differently. Whereas right now, it's quite difficult to really take an individual at their word, and there's no other way to, to observe and to understand that. And so I do think it's a really interesting counterpoint to, to be able to have that observation that objective data as well.
Steven Parton 37:04
So as that form factor that you showed us how available now?
Tan Le 37:08
Yes, it's we're about to launch it actually. We've been working with partners for the last few years, and, and now we're about to take it to the consumer. So yeah, we're really excited about it, I think that this sort of form factor is the future of neurotech. It's, it's really streamlined, it's seamless. Obviously, it does have, again, with any sort of optimization that we have to do, it's much more streamlined system, and so it has less channels, which means that we have to really rely on the heavy lifting of that decade of work that we've done in the past with these multi channel systems in order to create optimal mapping onto a very low count system like this. And it's it's an evolutionary process. So we were going out of the box out into the market, a limited subset of, of detections that's robust for this form factor. And hopefully, over time, we can start to scale that up. Obviously, not everything will map nicely onto this form factor. But we hope that it's a, it's an interesting enough platform that will allow us to start to slowly introduce new detections over time, because that's, that's the key is to not, I don't want to oversell what a skeletal system like this would be able to achieve. And so we've really focused in on the things that you can do well, right off the bat, and, and really wait on introducing too many things into the, into this form factor because it is much more streamlined than what we've traditionally used, which is we've got, we have a five channel, a 14 channel and a 32 channel system. So we have the luxury of having a lot more data from really observing all parts of the brain. So all of the key functional brain areas of the brain, whereas this one is a two channel system, you do get the left and the right hemispheres, which is which is nice to get that sort of a simple asymmetry information when needed. But it's still a lot less data than we're used to. So we've been much more cautious with this. Not not wanting to promise the world with this form factor.
Steven Parton 39:25
So you're going to see on a headset like that, or the year p so you're going to see something closer to maybe like an app with levels of stress levels of focus. Like what what kind of things yeah,
Tan Le 39:37
that's right. That's right. We're really about trying to achieve balance with that. So it's essentially a brain your brain coach, right? But but not in using real neuroscience data. So bringing a lot of things that everyone's doing in their normal life already. Whether it's working, whether it's doing breathing exercises for meditation, whether it's going out for a walk Whatever it is that you might be doing, but we're bringing the neuro insights into that equation and providing the data that helps you drive your decision. So because the machine learning algorithms are backing behind all of this, we will observe how you do all of these activities in your normal life. And they will be able to start to say, you know what, Steven, you're not a meditator, it's not working for you. This works really well for you instead. And so like any coach, you've got to do the hard work, but it's that it's able to identify what works for you, and to help you fine tune that experience even more, so you get better at what it is that you're doing. And so that's the idea with this is that yes, you know, you, you've got all of these things that you're doing every day, you're trying a lot of different things. In fact, there's a lot of people now that have that recognize if need for balance, they're doing their focus activities, whether through your flow state of flow, or focus, time timer, or like a pomodoro type methodology, that taking breaks, but there's no way to quantify it. And I kind of feel better after God for work. But I don't know how this is affecting me. How What about if I spend time with my daughter just hanging out with her? Does that restore my brain in the same way? Or should I just hang out with her in the garden? Or should I just play in her room? What is the difference between all of these activities, and so once you really start to know, actually, these are the best things that work for you. And you really only need a 15 or 20 minute break, in order to really get back to a good restaurateurs date, that's really helpful, because then I can really optimize my time to personalize my data myself, right? It's not about it really not about caring about what works for my husband, because I know your cycle is completely different to mine. But it's really nice to know what uniquely works for me, and then knowing that, that I've got data that backs that up so that I'm not just doing stuff, experimenting on myself, and then trying to say, I think that worked better. And we're constantly trying to figure out what's the best way to do it. Now I actually have data driven insight that observing my brain, not just observing my activity and what I'm doing, but it's observing how my brain is tackling the task. What does attentional networks look like? How well is it able to focus? How much stress reduction results from these activities so that then I can say, wow, that really worked for me, I'm going to keep doing that. And this is my optimal thing. And the thing is, your brain will change. And so if you habitual eyes towards something too much, maybe it will start prompting you to try something different. Because you do need to find that, that that optimal curve of just a little bit of challenge so that you can stay at your peak, which is really nice that I always I think there's a lot of us out there that wants to perform well. And whatever it is that we're doing, it's not just all about work, sometimes it's just about a better version of ourselves. And it'd be great to have something that can can help. With that whole process,
Steven Parton 43:03
I can see couples sitting down and comparing their data to be like, when you're stressed, let's not during this time,
Tan Le 43:12
stay away from me, that's your me time. This is my worst version of myself. Oh, that's so true. That's really funny.
Steven Parton 43:24
You mentioned meditation in there, have you noticed a difference in the kind of data that you get with people who have maybe done something like psychedelics or who are practiced meditators
Tan Le 43:37
we've looked at, we've looked at some studies with people who meditate for long periods of time, so we like to use them what we call a gold standard, to try and really benchmark how the general population is able to get to that and we actually have, how far away they are from achieving that, that gold standard. And there's definitely a very big difference in people who meditate for long periods of time who are experienced meditators. You know, what we found is that their ability to control their brain is at a level of sophistication that that is very hard to achieve for normal people, which I, as much as I, you know, I think it's, it's really amazing that a lot of people are starting to try and meditate I think it's quite a challenging practice to, to to get right and so but we have seen that there is real measurable benefits from from the practice of of meditation just because you're able to control and have mastery over your own mind. And, and I can see that in terms of just the electrical activities of the brain and how they're able to just manipulate it at will which is really quite a phenomenal idea that you have this system That fire is at all at this crazy pace, all the time, and you're able to just control that passed it down and, and get it shifted states with a lot of with a lot of control. And that's the that's the key. And I think that's really, it's really impressive. It's It was quite fascinating to me when we first looked at the brains of very experienced meditators. And so yeah, I definitely have a big believer in the practice, I just think that it's important to get that feedback to help you make sure that you're doing it correctly as well, because I, I'd hate to be spending, you know, 1520 minutes of trying to thinking that I'm doing the right thing, but I'm still a long way from, from what the brain should look like. And so it's nice to actually have that, that affirmation, as well.
Steven Parton 45:56
Absolutely. So what is the future of this technology look like for you, I mean, obviously, this new form factor is amazing, but what what's the future goals or hopes that you have for a motive or the technology in general?
Tan Le 46:11
Yeah, so my hope at the end of the day, is to really allow us to empower neuroscience research so that ultimately, we can connect humans and their brains to the world around us. And so that it becomes a really an environment becomes an extension of your brain, and we're no longer separate entities anymore. So the idea is that we are going to move into a world where it will be a very intelligent system, it will be it's surrounded by intelligent devices, intelligent environments, and we should be it should be responding to us in a humanistic intelligence type way. So I would like to see the environment become an extension of our brain, we are, you know, we have a way to connect to it in a very symbiotic fashion so that you walk into an environment, it knows you it personalizes the environment to you, it does things without you having to tell it what to do. So you know, when I need a coffee, it will start brewing the coffee rather than me having to go down and do all of the work. So I can, I can start to see an environment in the future where I walk into a street and I'm kind of confused. My my earbuds actually say, Oh, you know, this is, this is where you want to go next, right? This is if I've already started my map, and it knows that I'm confused, it'll actually prompt instead of me having to grab my phone and do whatever else and find that thing. Again, it becomes a lot more seamless. And that's that's the future I think we're not too far away from. And then the long term future is really going to be about trying to use the insights that we have from, from all of this activity of being able to connect seamlessly with our environment, to really optimize the health and well being of the brain. So that we can have a healthier brain, for the duration of however long we're going to end up living. Because ultimately, that's my goal, I don't want to live until 120, if I don't have my faculties, if I can't really enjoy the time with my grandkids, or have a really interesting conversation with my, my neighbor, I want to be able to do those things. If I'm going to live that long, otherwise, it's just not worth it. And so I know how important my brain is to the quality of life, that of the future. And so ultimately, all of these things are tools, mechanisms that allow us to create that really important connection with our brain in order for us to better understand its working, better understand its function, and then hopefully, empower us to improve the functioning of the brain over time so that we can enjoy, you know, the rest of the amazing things that that everybody else is creating around the world for us to allow us to, to enjoy life for much longer time. Hopefully,
Steven Parton 49:09
that's a worthy goal that I can get behind. Well, I really want to thank you for your time. Is there anything you'd like to point people towards before we wrap up here, maybe how to find your emotive headsets, any information that you want to talk about?
Tan Le 49:22
Yeah, so I have a book out it's called the neuro degeneration, you can pick that up and it's not just about eg technology, it covers the entire space of neuro technology. That's really exciting to me. I also, you know, please check out your motives calm. So we have a lot of interesting tools out there, especially for neuroscience researchers, but also for the enthusiast as well. And emotive is stopped without the letter E at the end.
Steven Parton 49:48
Perfect. We'll have all that in the show notes just to make it easier for everybody
Tan Le 49:51
and thanks so much, Steven.