The widespread diffusion of screen-based devices in adolescence has fueled a debate about the beneficial and detrimental effects on adolescents' well-being and development. With the aim of summarizing the existing literature on the associations between screen time (including Internet-related addictions) and adolescent brain development, the present scoping review summarized evidence from 16 task-unrelated and task-related neuroimaging studies, published between 2010 and 2020. Results highlight three important key messages: (i) a frequent and longer duration of screen-based media consumption (including Internet-related addictive behaviors) is related to a less efficient cognitive control system in adolescence, including areas of the Default Mode Network and the Central Executive Network; (ii) online activities act as strong rewards to the brain and repeated screen time augments the tendency to seek short-term gratifications; and (iii) neuroscientific research on the correlates between screen time and adolescent brain development is still at the beginning and in urgent need for further evidence, especially on the underlying causality mechanisms. Methodological, theoretical, and conceptual implications are discussed.

Objective measurements of engagement are increasingly sought after by both the media industry and scholar communities to explain what drives people to consume audiovisual contents. However, engagement is a complex construct that, at the psychological level, has been mainly operationalised through indicators of attentional and emotional processes, often overlooking motivational factors. We claim that in the context of news consumption, motivation, operationalised as intrinsic interest for consuming a given content, needs to be factored in together with attentional and emotional processes. The present work provides an objective metric for motivation based on electroencephalographic (EEG) registration of users’ neural activity, while they read sets of news pre-classified in terms of their potential interest. We focus on a metric that has been used as an indicator of the degree to which an item or event induces the motivation to approach or escape, the so called frontal alpha asymmetry (FAA). Moreover, in addition to the traditional approach to the analysis of EEG signals, we also introduce a more novel technique based on estimating the entropy of the signals. Results confirm that FAA is indeed a good proxy for objective monitoring of interest in media contents and that entropy analysis, although its interpretation in terms of information processing warrants further investigation, is also sensitive to the manipulation of interest, providing results that complement traditional power spectrum analysis.

Engagement is a vital metric in the advertising industry and its automatic estimation has huge commercial implications. This work presents a basic and simple framework for engagement estimation using EEG (electroencephalography) data specifically recorded while watching advertisement videos, and is meant to be a first step in a promising line of research. The system combines recent advances in low cost commercial Brain-Computer Interfaces with modeling user engagement in response to advertisement videos. We achieve an F1 score of nearly 0.7 for a binary classification of high and low values of self-reported engagement from multiple users. This study illustrates the possibility of seamless engagement measurement in the wild when interacting with media using a non invasive and readily available commercial EEG device. Performing engagement measurement via implicit tagging in this manner with a direct feedback from physiological signals, thus requiring no additional human effort, demonstrates a novel and potentially commercially relevant application in the area of advertisement video analysis.

Neuroforecasting predicts population-wide choices based on neural data of individuals and can be used, for example, in neuromarketing to estimate campaign successes. To deliver true value, the brain activity metrics should deliver predictive value above and beyond traditional stated preferences. Evidence from movie trailer research has proposed neural synchrony, which compares the similarity of brain responses across participants and has shown to be a promising tool in neuroforecasting for movie popularity. The music industry might also benefit from these increasingly accurate success predictors, but only one study has been forecasting music popularity, using functional magnetic resonance imaging measures. Current research validates the strength of neural synchrony as a predictive measure for popularity of music, making use of electroencephalogram to capture moment-to-moment neural similarity between respondents while they listen to music. Neural synchrony is demonstrated to be a significant predictor for public appreciation on Spotify 3 weeks and 10 months after the release of the albums, especially when combined with the release of a single. On an individual level, other brain measures were shown to relate to individual subjective likeability ratings, including Frontal Alpha Asymmetry and engagement when combined with the factors artist and single release. Our results show the predictive value of brain activity measures outperforms stated preferences. Especially, neural synchrony carries high predictive value for the popularity on Spotify, providing the music industry with an essential asset for efficient decision making and investments, in addition to other practical implications that include neuromarketing and advertising industries.

Background and objective Extensive use of social media raises concerns regarding its psychological and neurophysiological impact. Although behavioral effects have been the focus of earlier research, there are scarce empirical data addressing the degree to which real-time brain activity alters with social media use. This research aimed to examine the neurocognitive impact of social media usage by assessing brainwave activity via electroencephalography (EEG) to determine specific patterns of neural engagement as well as cognitive/emotional responses. Methods EEG recordings were obtained from 100 participants with a 24-channel system based on the 10-20 international standard. Participants were healthy adults aged 18-45 years (mean age: 27.4 years), including 52 females and 48 males. Individuals with a history of neurological or psychiatric disorders were excluded. Data were preprocessed with band-pass and notch filtering, artifact rejection by independent component analysis (ICA), common average referencing, epoching, and downsampling. Participants used social media for 30-minute periods, during which neural activity in five frequency bands (Delta, Theta, Alpha, Beta, Gamma) was recorded and analyzed in terms of user interactions and content type. Results Social media use caused marked alterations in brainwave activity. Alpha waves declined during engagement, especially with emotionally charged content, which suggests cognitive load and excitation. Beta and Gamma waves are heightened during active interaction and continue after engagement, which may indicate extended cognitive excitation and emotional engagement. Theta and Delta waves increased slightly during passive surfing or extended use, which could indicate introspection and mental exhaustion. Regional examination identified Beta/Gamma predominance in prefrontal and occipital cortices in decision-making and viewing of visual content, and Beta/Theta in parietal cortex during multitasking between platforms. Conclusions Our findings show that social media engages in brain reward pathways akin to those seen in addictive behavior, with extended Beta and Gamma activity having the potential to interfere with emotional regulation and attention. These neurophysiological consequences, especially delayed Alpha recovery and increased Delta activity, could bring to the fore new concerns regarding digital fatigue and mental health. The research indicates the importance of platform design interventions and additional longitudinal investigations.

<h4>Background</h4>Research on music-induced emotion and brain activity is constantly expanding. Although studies using inter-subject correlation (ISC), a collectively shared brain activity analysis method, have been conducted, whether ISC during music listening represents the music preferences of a large population remains uncertain; additionally, it remains unclear which factors influence ISC during music listening. Therefore, here, we aimed to investigate whether the ISCs of electroencephalography (EEG) during music listening represent a preference for music reflecting engagement or interest of a large population in music.<h4>Methods</h4>First, we selected 21 pieces of music from the Billboard Japan Hot 100 chart of 2017, which served as an indicator of preference reflecting the engagement and interest of a large population. To ensure even representation, we chose one piece for every fifth song on the chart, spanning from highly popular music to less popular ones. Next, we recorded EEG signals while the subjects listened to the selected music, and they were asked to evaluate four aspects (preference, enjoyment, frequency of listening, and arousal) for each song. Subsequently, we conducted ISC analysis by utilizing the first three principal components of EEG, which were highly correlated across subjects and extracted through correlated component analysis (CorrCA). We then explored whether music with high preferences that reflected the engagement and interest of large population had high ISC values. Additionally, we employed cluster analysis on all 21 pieces of music, utilizing the first three principal components of EEG, to investigate the impact of emotions and musical characteristics on EEG ISC during music listening.<h4>Results</h4>A significant distinction was noted between the mean ISC values of the 10 higher-ranked pieces of music compared to the 10 lower-ranked pieces of music [<i>t</i>(542) = -1.97, <i>p</i> = 0.0025]. This finding suggests that ISC values may correspond preferences reflecting engagement or interest of a large population. Furthermore, we found that significant variations were observed in the first three principal component values among the three clusters identified through cluster analysis, along with significant differences in arousal levels. Moreover, the characteristics of the music (tonality and tempo) differed among the three clusters. This indicates that the principal components, which exhibit high correlation among subjects and were employed in calculating ISC values, represent both subjects' arousal levels and specific characteristics of the music.<h4>Conclusion</h4>Subjects' arousal values during music listening and music characteristics (tonality and tempo) affect ISC values, which represent the interest of a large population in music.

Every day, humans interact with text spanning from different sources such as news, literature, education, and even social media. While reading, humans process text word by word, accessing the meaning of a particular word from the lexicon, and when needed, changing its meaning to match the context of the text (Harley, 2014). The process of reading can induce a range of emotions, such as engagement, confusion, frustration, surprise or happiness. For example, when readers come across unfamiliar jargon, this may confuse them, as they try to understand the text. In the past, scientists have addressed the emotion in text from a writer's perspective. For example the field of Sentiment Analysis, aims to detect the emotional charge of words, to infer the intentions of the writer. However, here we propose the reverse approach: detect emotions produced on readers while processing text. Detecting which emotions are induced by reading a piece of text can give us insights about the nature of the text itself. A word-emotion detector can be used to assign specific emotions experienced by readers to specific words or passages of text. This area of research has never been explored before. There are many potential applications to a word-emotion detector. For example, a word-emotion detector can be used to analyze how passages in books, news or social media are perceived by readers. This can guide stylistic choices to cater for a particular audience. In a learning environment, it can be used to