We performed simultaneous recordings of electroencephalography (EEG) from multiple students in a classroom, and measured the inter-subject correlation (ISC) of activity evoked by a common video stimulus. The neural reliability, as quantified by ISC, has been linked to engagement and attentional modulation in earlier studies that used high-grade equipment in laboratory settings. Here we reproduce many of the results from these studies using portable low-cost equipment, focusing on the robustness of using ISC for subjects experiencing naturalistic stimuli. The present data shows that stimulus-evoked neural responses, known to be modulated by attention, can be tracked for groups of students with synchronized EEG acquisition. This is a step towards real-time inference of engagement in the classroom.

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Correlated component analysis as proposed by Dmochowski et al. (2012) is a tool for investigating brain process similarity in the responses to multiple views of a given stimulus. Correlated components are identified under the assumption that the involved spatial networks are identical. Here we propose a hierarchical probabilistic model that can infer the level of universality in such multi-view data, from completely unrelated representations, corresponding to canonical correlation analysis, to identical representations as in correlated component analysis. This new model, which we denote Bayesian correlated component analysis, evaluates favourably against three relevant algorithms in simulated data. A well-established benchmark EEG dataset is used to further validate the new model and infer the variability of spatial representations across multiple subjects.

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BACKGROUND Clinical trials are expensive why it should be a priority to acquire as much data as possible during the trial. The burden on participants and staff is often the limiting factor on the amount of data feasibly acquired, which is why trials may benefit from incorporating the readily available small sensor-packed ubiquitous device; the smartphone. OBJECTIVE The aim of this study was to assess whether a smartphone can assist or replace existing practices in evaluating a physical activity intervention study in overweight sedentary adults. METHODS We introduce the smartphone as an additional sensing device in a physical activity intervention study that investigates the effects of active commuting and leisure-time exercise on a range of biological measures. RESULTS We find the smartphone is able to measure multiple modalities ubiquitously over a long duration and a hierarchical Bayesian analysis reveal estimates that are well in line with an independent analysis of the biological measures. CONCLUSIONS The smartphone has in this study shown that it, while not being without limitations, is able to augment current research methodologies and add value in historically infeasible ways. We can now ask questions that factorizes temporally on a minute-scale resolution and conceptually over the domains of everyday life. CLINICALTRIAL Clinicaltrials.gov NCT01962259