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Term-Relevance Prediction from Brain Signals (TRPB) is proposed to automatically detect relevance of text information directly from brain signals. An experiment with forty participants was conducted to record neural activity of participants while providing relevance judgments to text stimuli for a given topic. High-precision scientific equipment was used to quantify neural activity across 32 electroencephalography (EEG) channels. A classifier based on a multi-view EEG feature representation showed improvement up to 17% in relevance prediction based on brain signals alone. Relevance was also associated with brain activity with significant changes in certain brain areas. Consequently, TRPB is based on changes identified in specific brain areas and does not require user-specific training or calibration. Hence, relevance predictions can be conducted for unseen content and unseen participants. As an application of TRPB we demonstrate a high-precision variant of the classifier that constructs sets of relevant terms for a given unknown topic of interest. Our research shows that detecting relevance from brain signals is possible and allows the acquisition of relevance judgments without a need to observe any other user interaction. This suggests that TRPB could be used in combination or as an alternative for conventional implicit feedback signals, such as dwell time or click-through activity.

To understand how implicit and explicit biofeedback work in games, we developed a first-person shooter (FPS) game to experiment with different biofeedback techniques. While this area has seen plenty of discussion, there is little rigorous experimentation addressing how biofeedback can enhance human-computer interaction. In our two-part study, (N=36) subjects first played eight different game stages with two implicit biofeedback conditions, with two simulation-based comparison and repetition rounds, then repeated the two biofeedback stages when given explicit information on the biofeedback. The biofeedback conditions were respiration and skin-conductance (EDA) adaptations. Adaptation targets were four balanced player avatar attributes. We collected data with psychophysiological measures (electromyography, respiration, and EDA), a game experience questionnaire, and game-play measures.According to our experiment, implicit biofeedback does not produce significant effects in player experience in an FPS game. In the explicit biofeedback conditions, players were more immersed and positively affected, and they were able to manipulate the game play with the biosignal interface. We recommend exploring the possibilities of using explicit biofeedback interaction in commercial games.

Digital monitoring of physiological signals can allow computer systems to adapt unobtrusively to users, so as to enhance personalised 'smart' interactions. In recent years, physiological computing has grown as a research field, and it is increasingly considered in diverse applications, ranging from specialised work contexts to consumer electronics. Working in this emerging field requires comprehension of several physiological signals, psychophysiological states or 'indices', and analysis techniques. The resulting literature encompasses a complex array of knowledge and techniques, presenting a clear challenge to the practitioner.We provide a foundational review of the field of psychophysiology to serve as a primer for the novice, enabling rapid familiarisation with the core concepts, or as a quick-reference resource for advanced readers. We place special emphasis on everyday human-computer interface applications, drawing a distinction from clinical or sports applications, which are more commonplace. The review provides a framework of commonly understood terms associated with experiential constructs and physiological signals. Then, 12 short and precisely focused review chapters describe 10 individual signals or signal sources and present two technical discussions of online data fusion and processing. A systematic review of multimodal studies is provided in the form of a reference table. We conclude with a general discussion of the application of psychophysiology to human-computer interaction, including guidelines and challenges.

In daily life, we often copy the gestures and expressions of those we communicate with, but recent evidence shows that such mimicry has a physiological counterpart: interaction elicits linkage, which is a concordance between the biological signals of those involved. To find out how the type of social interaction affects linkage, pairs of participants played a turn-based computer game in which the level of competition was systematically varied between cooperation and competition. Linkage in the beta and gamma frequency bands was observed in the EEG, especially when the participants played directly against each other. Emotional expression, measured using facial EMG, reflected this pattern, with the most competitive condition showing enhanced linkage over the facial muscle-regions involved in smiling. These effects were found to be related to self-reported social presence: linkage in positive emotional expression was associated with self-reported shared negative feelings. The observed effects confirmed the hypothesis that the social context affected the degree to which participants had similar reactions to their environment and consequently showed similar patterns of brain activity. We discuss the functional resemblance between linkage, as an indicator of a shared physiology and affect, and the well-known mirror neuron system, and how they relate to social functions like empathy.

The use of psychophysiologic signals in human-computer interaction is a growing field with significant potential for future smart personalised systems. Working in this emerging field requires comprehension of different physiological signals and analysis techniques. Cardiovascular signals such as heart rate variability and blood pressure variability are commonly used in psychophysiology in order to investigate phenomena such as mental workload. In this paper we present a short review of different cardiovascular metrics useful in the context of humancomputer interaction. This paper aims to serve as a primer for the novice, enabling rapid familiarisation with the latest core concepts. We emphasise everyday humancomputer interface applications to distinguish from the more common clinical or sports uses of psychophysiology. This paper is an extract from a comprehensive review of the entire field of ambulatory psychophysiology, with 12 similar chapters, plus application guidelines and systematic review.

We present physiological text annotation, which refers to the practice of associating physiological responses to text content in order to infer characteristics of the user information needs and affective responses. Text annotation is a laborious task, and implicit feedback has been studied as a way to collect annotations without requiring any explicit action from the user. Previous work has explored behavioral signals, such as clicks or dwell time to automatically infer annotations, and physiological signals have mostly been explored for image or video content. We report on two experiments in which physiological text annotation is studied first to 1) indicate perceived relevance and then to 2) indicate affective responses of the users. The first experiment tackles the user's perception of relevance of an information item, which is fundamental towards revealing the user's information needs. The second experiment is then aimed at revealing the user's affective responses towards a -relevant-text document. Results show that physiological user signals are associated with relevance and affect. In particular, electrodermal activity (EDA) was found to be different when users read relevant content than when they read irrelevant content and was found to be lower when reading texts with negative emotional content than when reading texts This paper or a similar version is not currently under review by a journal or conference, nor will it be submitted to such within the next three months. This paper is void of plagiarism or self-plagiarism as defined in Section 1 of ACM's Policy and Procedures on Plagiarism. with neutral content. Together, the experiments show that physiological text annotation can provide valuable implicit inputs for personalized systems. We discuss how our findings help design personalized systems that can annotate digital content using human physiology without the need for any explicit user interaction.

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