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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The October 2015 edition of IEEE Computer magazine is devoted to the topic of Physiological Computing. Giulio Jacucci, myself and Erin Solovey acted as co-editors and the introduction for the magazine is available here.
The paper included in the special issue cover a range of topics, including: measurement of stress in VR, combining pupilometry with EEG to detect changes in operator workload and using mobile neuroimaging to create attention-aware technologies.
There is also a podcast associated with the SI featuring the guest editors in conversation with Robert Jacobs from Tufts University on current topics and future directions in Physiological Computing – you can hear it here.
I am one of the co-editors of a special issue of the Interacting With Computers, which is now available online here. The title for the special issue is Physiological Computing for Intelligent Adaptation, it contains five full research papers covering a range of topics such as: use of VR for stress reduction, mental workload monitoring and a comparison of EEG headsets.
A quick post to alert people to the first forum for the Community for Passive BCI Research that take place from the 16th to the 18th of July at the Hanse Institute for Advanced Study in Delmenhorst, near Bremen, Germany. This event is being organised by Thorsten Zander from the Berlin Institute of Technology.
The main aim of the forum in his own words “is to connect researchers in this young field and to give them a platform to share their motivations and intentions. Therefore, the focus will not be primarily set on the presentation of new scientific results, but on the discussion of current and future directions and the possibilities to shape the community.”
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It was way back in 2011 during our CHI workshop that we first discussed the possibility of putting together an edited collection for Springer on the topic of physiological computing. It was clear to me at that time that many people associated physiological computing with implicit monitoring as opposed the active control that characterised BCI. When we had the opportunity to put together a collection, one idea was to extend the scope of physiological computing to include all technologies where signals from the brain and the body were used as a form of input. Some may interpret this relabelling of physiological computing as an all-inclusive strategy as a provocative move. But we did not take this option as a conceptual ‘land-grab’ but rather an attempt to be as inclusive as possible and to bring together what I still perceive to be a rather disparate and fractured research community. After all, we are all using psychophysiology in one form or another and share a common interest in sensor design, interaction mechanics and real-time measurement.
The resulting book is finally close to publication (tentative date: 4th April 2014) and you can follow this link to get the full details. We’re pleased to have a wide range of contributions on an array of technologies, from eye input to digital memories via mental workload monitoring, implicit interaction, robotics, biofeedback and cultural heritage. Thanks to all our contributors and the staff at Springer who helped us along the way.
Last week I attended the first international conference on physiological computing held in Lisbon. Before commenting on the conference, it should be noted that I was one of the program co-chairs, so I am not completely objective – but as this was something of a watershed event for research in this area, I didn’t want to let the conference pass without comment on the blog.
The conference lasted for two-and-a-half days and included four keynote speakers. It was a relatively small meeting with respect to the number of delegates – but that is to be expected from a fledgling conference in an area that is somewhat niche with respect to methodology but very broad in terms of potential applications.
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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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I am one of the organisers for a workshop event at ICMI 2012 entitled “BCI Grand Challenges.” The deadline for submissions was this coming Friday (15th) but has now been extended until the 30th June. Full details are below.
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With regards to the development of physiological computing systems, whether they are BCI applications or fall into the category of affective computing, there seems (to me) to be two distinct types of research community at work. The first (and oldest) community are university-based academics, like myself, doing basic research on measures, methods and prototypes with the primary aim of publishing our work in various conferences and journals. For the most part, we are a mixture of psychologists, computer scientists and engineers, many of whom have an interest in human-computer interaction. The second community formed around the availability of commercial EEG peripherals, such as the Emotiv and Neurosky. Some members of this community are academics and others are developers, I suspect many are dedicated gamers. They are looking to build applications and hacks to embellish interactive experience with a strong emphasis on commercialisation.
There are many differences between the two groups. My own academic group is ‘old-school’ in many ways, motivated by research issues and defined by the usual hierarchies associated with specialisation and rank. The newer group is more inclusive (the tag-line on the NeuroSky site is “Brain Sensors for Everyone”); they basically want to build stuff and preferably sell it.
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Way back in 2008, I was due to go to Florence to present at a workshop on affective BCI as part of CHI. In the event, I was ill that morning and missed the trip and the workshop. As I’d prepared the presentation, I made a podcast for sharing with the workshop attendees. I dug it out of the vaults for this post because gaming and physiological computing is such an interesting topic.
The work is dated now, but basically I’m drawing a distinction between my understanding of BCI and biocybernetic adaptation. The former is an alternative means of input control within the HCI, the latter can be used to adapt the nature of the HCI. I also argue that BCI is ideally suited certain types of game mechanics because it will not work 100% of the time. I used the TV series “Heroes” to illustrate these kinds of mechanics, which I regret in hindsight, because I totally lost all enthusiasm for that show after series 1.
The original CHI paper for this presentation is available here.
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