In their article Activity Recognition for the Mind: Toward a Cognitive “Quantified Self,” researchers Kunze, Iwamura, and Kise, argue that it is possible to track the cognitive activities (as one would with physical activity), through means alternative to EEG, which uses difference in electrical potential that results from brain activities as manifest in brain waves. They present an alternative to EEG due to the disadvantage of it in their opinion, its invasiveness, noisiness compared to muscle movement trackers, and the effort and time it takes for the accompanying signal processing.
The alternative to EEG as the authors present it is the tracking of eye-movement, or EOG (or Electrooculography) which is arguably meaningfully correlated to cognition. For instance, comprehension of a reader of a word is proportionally related the amount of time in which the eye is fixated on it. Other information that can be collected through EOG is the degree of fatigue of the reader, as well as his level of attentiveness.
In addition, measuring the distance traversed by the eye’s saccade can be used to determine parameters related to the consumed media itself, including its type (a page in a magazine has a different saccade pattern than, say, a published article that follows the general scientific paper format), and the number of words read.
EOG, though cheaper than ECG methods, can also be intrusive. For instance, the need to measure the difference in electrical activity between the Retina and Cornea, requires that some kind of physical contact between the device and the skin area around the eye of the user is in effect at all times. An alternative would be to utilize infrared rays to measure the shape of the iris, which can even be combined with the measurement and tracking of facial features to get more accurate data.
EEG, while invasive, is still a good way to measure a person’s response to phenomena. According to NeuroPlace: Making Sense of a Place, a paper authored by Lulwa Al-Barrak and Eiman Kanjo, EEG has relevant applications such as quantifying cognition and state while learning, determining whether a driver is fatigued, and even tagging multimedia according to emotion.
To that effect, the authors combined mobile technology (for GPS location tracking and session marking) and the EEG parameters readily accessibly through NeuroSky, an EEG headset made by Mindwave Mobile, to measure a person’s mental “response” to a place. The headset readily measures the frequencies of various brainwaves, including delta, theta, and multiple intensities of alpha, beta, and theta waves), and accordingly measures the user’s state of “attention” or “meditation.”
An interesting piece of methodology in the work that the author did is the use is the correlation with environmental noise to remove noise from the signal. While no further details were provided on how they achieved that, the possibility of that offers a remedy to the long standing noise problem in EEG-based apparatus. (NeuroSky does reportedly provide information on the level of noise captured too). Furthermore, the authors were able to categorize places using statistical methods, including logistic regression. Each data point was also coupled with a timestamp label as well as GPS coordinates.
My main takeaway from both papers is that the phenomena related to the brain, whether in terms of cognition or mental states, are possible to track and monitor whether directly, through EEG, or indirectly through secondary channels such as gaze analysis or face features. Each method comes with its own shortcomings. For instance, EEG is prone to environmental noise, whereas gaze analysis is limited in the kind of data it can capture. In addition to that, both technologies have varying levels of invasiveness, which I believe is the main hurdle for this class of QS devices. It is a multifaceted problem that is under the influence of both technology (i.e. how small can the underlying circuitry be made without sacrificing the functionality?) and design (how can we best hide it all?) factors.