Differentiation Analysis of Continuous Electroencephalographic Activity Triggered by Video Clip Contents Armand Mensen 1,2 , William Marshall 2 , Shuntaro Sasai 2 , and Giulio Tononi 2 Abstract ■ While viewing a video clip, we experience a wide variety of contents, from low-level features of the images to high-level ideas such as the storyline. Each change in our experience must be supported by some corresponding change in neurophysio- logical activity. Differentiation analysis, which quantifies the differences in brain activity by measuring the distances be- tween observed brain states, was applied here to continuous high-density electroencephalographic data recorded while participants watched short video clips. These clips were manip- ulated in various ways to change the degree of meaningfulness of their contents. We found that neurophysiological differenti- ation mirrored that of phenomenal differentiation, being higher for meaningful clips and lower for phase-scrambled versions or random noise. The distinction between meaningful and mean- ingless clips was present even at the individual level, and more- over, differentiation values correlated with individual subjective reports of meaningfulness. Spatial and spectral breakdowns of the overall effect showed frontal and posterior ROIs and highlighted specific roles for different spectral bands. Compar- ing the results with a multivariate decoding approach reveals that the two methods are capturing different aspects of brain activity and highlights a crucial theoretical distinction between the level and pattern of activity. In future applications, differen- tiation analysis may be used to evaluate the subjective meaning- fulness of stimuli when behavioral responses may be inadequate, as with disorders of consciousness. ■ INTRODUCTION As we experience the world, or when watching a movie, we encounter a variety of objects and situations. Each moment of experience can be uniquely identified by its contents, which can range from simple, low-level, visual features, such as oriented edges, to complex, high-level, invariant ideas, such as faces, places, danger, and so on. On the other hand, we can also encounter inputs that are objectively different but subjectively indistinguishable, such as television noise. If a change in the inputs results in a change in corresponding experience, then this differ- ence can be considered as subjectively meaningful. These meaningful differences must be supported by distinct neural activity, and we further assume that the more dis- tinct the experiences are, the more differentiated the supporting activity should be. Differentiation analysis (DA) quantifies the changes in neurophysiological activity patterns associated with a given stimulus set (Mensen, Marshall, & Tononi, 2017). DA quantifies how distinct the patterns of activity are during presentation of a stim- ulus set. Differentiation will be zero if the activity pattern is consistent for the duration of the stimulus set, whereas high differentiation is achieved when the activity pattern is changing substantially from moment to moment. Thus, applying DA to neural activity evoked by a set of stimuli can provide an objective measure of the neural differen- tiation that must underlie the subjective meaningfulness of that particular stimulus set. In previous work, we employed fMRI to show that dif- ferentiation measures based on the complexity of neuro- physiological activation were higher when viewing short “Charlie Chaplin” clips compared with watching a scram- bled version of the clips and were lowest when watching television noise (Boly et al., 2015). This result was obtained despite similar overall levels of neurophysiological acti- vation and although the stimulus differentiation (e.g., pixel-to-pixel variability) was actually higher in the case of television noise. Subsequent work has demonstrated that stimulus set meaningfulness can also be captured by electroencephalography (EEG) using DA (Mensen et al., 2017). Participants were presented with static images from a number of meaningful categories (animals, food, people, etc.) as well as three meaningless categories (noise, phase- scrambled images, and overlapping disks). By measuring the multivariate distances between the evoked neural responses to each image, we showed that neurophysiolog- ical differentiation was higher for the meaningful compared with meaningless images. Differentiation was significant at the individual level and was most 1 University of Liege, 2 University of Wisconsin-Madison © 2018 Massachusetts Institute of Technology Journal of Cognitive Neuroscience X:Y, pp. 1–11 doi:10.1162/jocn_a_01278