I originally coined the term ‘physiological computing’ to describe a whole class of emerging technologies constructed around closed-loop control. These technologies collected implicit measures from the brain and body of the user, which informed a process of intelligent adaptation at the user interface.
If you survey research in this field, from mental workload monitoring to applications in affective computing, there’s an overwhelming bias towards the first part of the closed-loop – the business of designing sensors, collecting data and classifying psychological states. In contrast, you see very little on what happens at the interface once target states have been detected. The dearth of work on intelligent adaptation is a problem because signal processing protocols and machine learning algorithms are being developed in a vacuum – without any context for usage. This disconnect both neglects and negates the holistic nature of closed-loop control and the direct link between classification and adaptation. We can even generate a maxim to describe the relationship between the two:
the number of states recognised by a physiological computing system should be minimum required to support the range of adaptive options that can be delivered at the interface
This maxim minimises the number of states to enhance classification accuracy, while making an explicit link between the act of measurement at the first part of the loop with the process of adaptation that is the last link in the chain.
If this kind of stuff sounds abstract or of limited relevance to the research community, it shouldn’t. If we look at research into the classic ‘active’ BCI paradigm, there is clear continuity between state classification and corresponding actions at the interface. This continuity owes its prominence to the fact that the BCI research community is dedicated to enhancing the lives of end users and the utility of the system lies at the core of their research process. But to be fair, the link between brain activation and input control is direct and easy to conceptualise in the ‘active’ BCI paradigm. For those systems that working on an implicit basis, detection of the target state is merely the jumping off point for a complicated process of user interface design.
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Back in 2003, Lawrence Hettinger and colleagues penned this paper on the topic of neuroadaptive interface technology. This concept described a closed-loop system where fluctuations in cognitive activity or emotional state informs the functional characteristics of an interface. The core concept sits comfortably with a host of closed-loop technologies in the domain of physiological computing.
One great insight from this 2003 paper was to describe how neuroadaptive interfaces could enhance communication between person and system. They argued that human-computer interaction currently existed in an asymmetrical form. The person can access a huge amount of information about the computer system (available RAM, number of active operations) but the system is fundamentally ‘blind’ to the intentions of the user or their level of mental workload, frustration or fatigue. Neuroadaptive interfaces would enable symmetrical forms of human-computer interaction where technology can respond to implicit changes in the human nervous system, and most significantly, interpret those covert sources of data in order to inform responses at the interface.
Allowing humans to communicate implicitly with machines in this way could enormously increase the efficiency of human-computer interaction with respects to ‘bits per second’. The keyboard, mouse and touchscreen remain the dominant modes of input control by which we translate thoughts into action in the digital realm. We communicate with computers via volitional acts of explicit perceptual-motor control – the same asymmetrical/explicit model of HCI holds true for naturalistic modes of input control, such as speech and gestures. The concept of a symmetrical HCI based on implicit signals that are generated spontaneously and automatically by the user represents a significant shift from conventional modes of input control.
This recent paper published in PNAS by Thorsten Zander and colleagues provides a demonstration of a symmetrical, neuroadaptive interface in action.
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A couple of years ago we organised this CHI workshop on meaningful interaction in physiological computing. As much as I felt this was an important area for investigation, I also found the topic very hard to get a handle on. I recently revisited this problem in working on a co-authored book chapter with Kiel on our forthcoming collection for Springer entitled ‘Advances in Physiological Computing’ due out next May.
On reflection, much of my difficulty revolved around the complexity of defining meaningful interaction in context. For systems like BCI or ocular control, where input control is the key function, the meaningfulness of the HCI is self-evident. If I want an avatar to move forward, I expect my BCI to translate that intention into analogous action at the interface. But biocybernetic systems, where spontaneous psychophysiology is monitored, analysed and classified, are a different story. The goal of this system is to adapt in a timely and appropriate fashion and evaluating the literal meaning of that kind of interaction is complex for a host of reasons.
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There has been a lot of tweets and blogs devoted to an article written recently by Don Norman for the MIT Technology Review on wearable computing. The original article is here, but in summary, Norman points to an underlying paradox surrounding Google Glass etc. In the first instance, these technological artifacts are designed to enhance human abilities (allowing us to email on the move, navigate etc.), however, because of inherent limitations on the human information processing system, they have significant potential to degrade aspects of human performance. Think about browsing Amazon on your glasses whilst crossing a busy street and you get the idea.
The paragraph in Norman’s article that caught my attention and is most relevant to this blog is this one.
“Eventually we will be able to eavesdrop on both our own internal states and those of others. Tiny sensors and clever software will infer their emotional and mental states and our own. Worse, the inferences will often be wrong: a person’s pulse rate just went up, or their skin conductance just changed; there are many factors that could cause such things to happen, but technologists are apt to focus upon a simple, single interpretation.”
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It has been said that every cloud has a silver lining and the only positive from chronic jet lag (Kiel and I arrived in Vancouver yesterday for the CHI workshop) is that it does give you a chance to catch up with overdue tasks. This is a post I’d been meaning to write for several weeks about my involvement in the REFLECT project.
For the last three years, our group at LJMU have been working on a collaborative project called REFLECT funded by the EU Commission under the Future and Emerging Technology Initiative. This project was centred around the concept of “reflective software” that responds implicitly to changes in user needs and in real-time. A variety of physiological sensors are applied to the user in order to inform this kind of reflective adaptation. So far, this is regular fare for anyone who’s read this blog before, being a standard set-up for a biocybernetic adaptation system.
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I came across an article in a Sunday newspaper a couple of weeks ago about an artist called xxxy who has created an installation using a BCI of sorts. I’m piecing this together from what I read in the paper and what I could see on his site, but the general idea is this: person wears a portable EEG rig (I don’t recognise the model) and is placed in a harness with wires reaching up and up and up into the ceiling. The person closes their eyes and relaxes – presumably as they enter a state of alpha augmentation, they begin to levitate courtesy of the wires. The more that they relax or the longer they sustain that state, the higher they go. It’s hard to tell from the video, but the person seems to be suspended around 25-30 feet in the air.
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In last week’s excellent Bad Science article from The Guardian, Ben Goldacre puts his finger on a topic that I think is particularly relevant for physiological computing systems. He quotes press reports about MRI research into “hypoactive sexual desire response” – no, I hadn’t heard of it either, it’s a condition where the person has low libido. In this study women with the condition and ‘normals’ viewed erotic imagery in the scanner. A full article on the study from the Mail can be found here but what caught the attention of Bad Science is this interesting quote from one of the researchers involved: “Being able to identify physiological changes, to me provides significant evidence that it’s a true disorder as opposed to a societal construct.”
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The Emotiv system is a EEG headset designed for the development of brain-computer interfaces. It uses 12 dry electrodes (i.e. no gel necessary), communicates wirelessly with a PC and comes with a range of development software to create applications and interfaces. If you watch this 10min video from TEDGlobal, you get a good overview of how the system works.
First of all, I haven’t had any hands-on experience with the Emotiv headset and these observations are based upon what I’ve seen and read online. But the talk at TED prompted a number of technical questions that I’ve been unable to satisfy in absence of working directly with the system.
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I was reading this short article in The Guardian today about the failure of polygraph technologies (including fMRI versions and voice analysis) to deliver data that was sufficiently robust to be admissible in court as evidence. Several points made in the article prompted a thought that the development of physiological computing technologies, to some extent, live in the shadow of the polygraph.
Think about it. Both the polygraph and physiological computing aim to transform personal and private experience into quantifiable data that may be observed and assessed. Both capture unconscious physiological changes that may signify hidden psychological motives and agendas, subconscious or otherwise – and of course, both involve the attachment of sensor apparatus. The convergence between both technologies dictates that both are notoriously difficult to validate (hence the problems of polygraph evidence in court) – and that seems true whether we’re talking about the use of the P300 for “brain fingerprinting” or the use of ECG and respiration to capture a specific category of emotion.
Whenever I do a presentation about physiological computing, I can almost sense antipathy to the concept from some members of audience because the first thing people think about is the polygraph and the second group of thoughts that logically follow are concerns about privacy, misuse and spying. To counter these fears, I do point out that physiological computing, whether it’s a game or a means of adapting a software agent or a brain-computer interface, has been developed for very different purposes; this technology is intended for personal use, it’s about control for the individual in the broadest sense, e.g. to control a cursor, to promote reflection and self-regulation, to make software reactive, personalised and smarter, to ensure that the data protection rights of the individual are preserved – especially if they wish to share their data with others.
But everyone knows that any signal that can be measured can be hacked, so even capturing these kinds of physiological data per se opens the door for spying and other profound invasions of privacy.
Which takes us inevitably back in the shadow of the polygraph.
I’m sure attitudes will change if the right piece of technology comes along that demonstrates the up side of physiological computing. But if early systems don’t take data privacy seriously, as in very seriously, the public could go cold on this concept before the systems have had a chance to prove themselves in the marketplace.
For musings on a similar theme, see my previous post Designing for the Guillable.
I came across this article about the Heart Chamber Orchestra on the Wired site last week. The Orchestra are a group of musicians who wear ECG monitors whilst they play – the signals from the ECG feed directly into laptops and adapts the musical scores played directly and in real-time. They also have some nice graphics generated by the ECG running in the background when they play (see clip below). What I think is really interesting about this project is the reflexive loop set up between the ECG, the musician’s response and the adaptation of the musical score. It really goes beyond standard biofeedback – a live feed from the ECG mutates the musical score, the player responds to technical/emotional qualities of that score, which has a second-order effect on the ECG and so on. In the Wired article, they refer to the possibility of the audience being equipped with ECG monitors to provide another input to the loop – which is truly a mind-boggling possibility in terms of a fully-functioning biocybernetic loop.
The thing I find slightly frustrating about the article and the information contained in the project website is the lack of information about how the ECG influences the musical score. In a straightforward way, an ECG will yield a beat-to-beat interval, which of course could generate a metronomic beat if averaged over the group. Alternatively each individual ECG could generate its own beat, which could be superimposed over one another. But there are dozens of ways in which ECG information could be used to adapt a musical score in a real-time. According to the project information, there is also a composer involved doing some live manipulations of the score, but it’s hard to figure out how much of the real-time transformation is coming from him or her and how much is directly from the ECG signal.
I should also say that the Orchestra are currently competing for the FILE PRIX LUX prize and you can vote for them here
Before you do, you might want to see the orchestra in action in the clip below.
Heart chamber orchestra on vimeo