I always harbored two assumptions about the development of physiological computing systems that have only become apparent (to me at least) as technological innovation seems to contradict them. First of all, I thought nascent forms of physiological computing systems would be developed for desktop system where the user stays in a stationary and more-or-less sedentary position, thus minimising the probability of movement artifacts. Also, I assumed that physiological computing devices would only ever be achieved as coordinated holistic systems. In other words, specific sensors linked to a dedicated controller that provides input to adaptive software, all designed as a seamless chain of information flow.
I just watched a TEDMED talk about the iBrain device via this link on the excellent Medgadget resource. The iBrain is a single-channel EEG recording collected via ‘dry’ electrodes where the data is stored in a conventional handheld device such as a cellphone. In my opinion, the clever part of this technology is the application of mathematics to wring detailed information out of a limited data set – it’s a very efficient strategy.
The hardware looks to be fairly standard – a wireless EEG link to a mobile device. But its simplicity provides an indication of where this kind of physiological computing application could be going in the future – mobile monitoring for early detection of medical problems piggy-backing onto conventional technology. If physiological computing applications become widespread, this kind of proactive medical monitoring could become standard. And the main barrier to that is non-intrusive, non-medicalised sensor development.
In the meantime, Neurovigil, the company behind the product, recently announced a partnership with Swiss pharmaceutical giants Roche who want to apply this technology to clinical drug trials. I guess the methodology focuses the drug companies to consider covert changes in physiology as a sensitive marker of drug efficacy or side-effects.
I like the simplicity of the iBrain (1 channel of EEG) but speaker make some big claims for their analysis, the implicit ones deal with the potential of EEG to identify neuropathologies. That may be possible but I’m sceptical about whether 1 channel is sufficient. The company have obviously applied their pared-down analysis to sleep stages with some success but I was left wondering what added value the device provided compared to less-intrusive movement sensors used to analyse sleep behaviour, e.g. the Actiwatch