Examining the sense of agency in human-computer interaction

Humans are agents, we feel that we control the course of events on our everyday life. This refers to the Sense of Agency (SoA). This experience is not only crucial in our daily life, but also in our interaction with technology. When we manipulate a user interface (e.g., computer, smartphone, etc.), we expect that the system responds to our input commands with feedback, as we desire to feel that we are in charge of the interaction. If this interplay elicits a SoA, then the user will perceive an instinctive feeling of “I am controlling this”. Although research in Human-Computer Interaction (HCI) pursuits the design of intuitive and responsive systems, most of the current studies have been focussed mainly on interaction techniques (e.g., software-hardware) and User Experience (UX) (e.g., comfort, usability, etc.), and very little has been investigated in terms of the SoA i.e., the conscious experience of being in control regarding the interaction. In this thesis, we present an experimental exploration of the role of the SoA in interaction paradigms typical of HCI. After two chapters of introduction and related work, we describe a series of studies that explore agency implication in interaction with systems through human senses such as vision, audio, touch and smell. Chapter 3 explores the SoA in mid-air haptic interaction through touchless actions. Then, Chapter 4 examines agency modulation through smell and its application for olfactory interfaces. Chapter 5 describes two novel timing techniques based on auditory and haptic cues that provide alternative timing methods to the traditional Libet clock. Finally, we conclude with a discussion chapter that highlights the importance of our SoA during interactions with technology as well as the implications of the results found, in the design of user interfaces

Brain-Computer Interfaces for Non-clinical (Home, Sports, Art, Entertainment, Education, Well-being) Applications

HCI researchers interest in BCI is increasing because the technology industry is expanding into application areas where efficiency is not the main goal of concern. Domestic or public space use of information and communication technology raise awareness of the importance of affect, comfort, family, community, or playfulness, rather than efficiency. Therefore, in addition to non-clinical BCI applications that require efficiency and precision, this Research Topic also addresses the use of BCI for various types of domestic, entertainment, educational, sports, and well-being applications. These applications can relate to an individual user as well as to multiple cooperating or competing users. We also see a renewed interest of artists to make use of such devices to design interactive art installations that know about the brain activity of an individual user or the collective brain activity of a group of users, for example, an audience. Hence, this Research Topic also addresses how BCI technology influences artistic creation and practice, and the use of BCI technology to manipulate and control sound, video, and virtual and augmented reality (VR/AR)

Deep fusion of multi-channel neurophysiological signal for emotion recognition and monitoring

How to fuse multi-channel neurophysiological signals for emotion recognition is emerging as a hot research topic in community of Computational Psychophysiology. Nevertheless, prior feature engineering based approaches require extracting various domain knowledge related features at a high time cost. Moreover, traditional fusion method cannot fully utilise correlation information between different channels and frequency components. In this paper, we design a hybrid deep learning model, in which the 'Convolutional Neural Network (CNN)' is utilised for extracting task-related features, as well as mining inter-channel and inter-frequency correlation, besides, the 'Recurrent Neural Network (RNN)' is concatenated for integrating contextual information from the frame cube sequence. Experiments are carried out in a trial-level emotion recognition task, on the DEAP benchmarking dataset. Experimental results demonstrate that the proposed framework outperforms the classical methods, with regard to both of the emotional dimensions of Valence and Arousal

Towards EEG-based BCI driven by emotions for addressing BCI-Illiteracy: a meta-analytic review

Many critical aspects affect the correct operation of a Brain Computer Interface. The term BCI-illiteracy' describes the impossibility of using a BCI paradigm. At present, a universal solution does not exist and seeking innovative protocols to drive a BCI is mandatory. This work presents a meta-analytic review on recent advances in emotions recognition with the perspective of using emotions as voluntary, stimulus-independent, commands for BCIs. 60 papers, based on electroencephalography measurements, were selected to evaluate what emotions have been most recognised and what brain regions were activated by them. It was found that happiness, sadness, anger and calm were the most recognised emotions. Relevant discriminant locations for emotions recognition and for the particular case of discrete emotions recognition were identified in the temporal, frontal and parietal areas. The meta-analysis was mainly performed on stimulus-elicited emotions, due to the limited amount of literature about self-induced emotions. The obtained results represent a good starting point for the development of BCI driven by emotions and allow to: (1) ascertain that emotions are measurable and recognisable one from another (2) select a subset of most recognisable emotions and the corresponding active brain regions