Using Brain -Computer Interface to evaluate the User eXperience in interactive systems

Cano, Sandra; Soto, Jonathan; Acosta, Laura; Peñeñory, Victor M.; Moreira, Fernando

doi:10.1080/21681163.2022.2072398

Cited by 5 publications

(3 citation statements)

References 34 publications

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“…A common pattern has emerged from studies in which EEG is the most used signal to measure central nervous system activity, with fMRI closely behind it-the latter being more extensively used in the context of interpersonal trust [35]. EEG analysis is increasingly being utilized in human-robot interaction evaluation and brain-computer interfaces [36], as it provides the means to create real-time non-interruptive evaluation systems enabling the assessment of human mental states such as attention, workload, and fatigue during interaction [37][38][39]. Additionally, attempts have been made to study trust through EEG measurements which only look at event-related potentials (ERPs), but ERP has proven to be unsuitable for real-time trust level sensing during human-machine interaction due to the difficulty in identifying triggers [33,39].…”

Section: Trust Measuring Using Different and Combined Psychophysiolog...mentioning

confidence: 99%

Modelling and Measuring Trust in Human–Robot Collaboration

Loizaga,

Bastida,

Sillaurren

et al. 2024

Applied Sciences

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Recognizing trust as a pivotal element for success within Human–Robot Collaboration (HRC) environments, this article examines its nature, exploring the different dimensions of trust, analysing the factors affecting each of them, and proposing alternatives for trust measurement. To do so, we designed an experimental procedure involving 50 participants interacting with a modified ‘Inspector game’ while we monitored their brain, electrodermal, respiratory, and ocular activities. This procedure allowed us to map dispositional (static individual baseline) and learned (dynamic, based on prior interactions) dimensions of trust, considering both demographic and psychophysiological aspects. Our findings challenge traditional assumptions regarding the dispositional dimension of trust and establish clear evidence that the first interactions are critical for the trust-building process and the temporal evolution of trust. By identifying more significant psychophysiological features for trust detection and underscoring the importance of individualized trust assessment, this research contributes to understanding the nature of trust in HRC. Such insights are crucial for enabling more seamless human–robot interaction in collaborative environments.

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Section: Trust Measuring Using Different and Combined Psychophysiolog...mentioning

confidence: 99%

Modelling and Measuring Trust in Human–Robot Collaboration

Loizaga,

Bastida,

Sillaurren

et al. 2024

Applied Sciences

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“…Specifically, due to its non-invasive and portable characteristics, electroencephalography (EEG)-based BCI has become the most popular form among other neural recording modalities [56]. Endeavors have been made using EEG-based BCI to evaluate interactive systems (e.g., [57]). In these applications, the measurement of human mental states such as workload, attention, and emotions has been shown to benefit a lot from the EEG technique [55].…”

Section: Real-time Evaluation Of Hri With Bcimentioning

confidence: 99%

Inspiring Real-Time Evaluation and Optimization of Human–Robot Interaction with Psychological Findings from Human–Human Interaction

2023

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The increasingly central role of robotic agents in daily life requires effective human–robot interaction (HRI). For roboticists to optimize interaction design, it is crucial to understand the potential effects of robotic agents on human performance. Yet a systematic specification of contributing factors is lacking, and objective measures of HRI performance are still limited. In these regards, the findings of research on human–human interaction can provide valuable insights. In this review, we break down the complex effects of robotic agents on interacting humans into some basic building blocks based on human–human interaction findings, i.e., the potential effects of physical presence, motor actions, and task co-representation in HRI. For each effect, we advise on future directions regarding its implication. Furthermore, we propose that the neural correlates of these effects could support real-time evaluation and optimization of HRI with electroencephalograph (EEG)-based brain–computer interface (BCI).

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“…The primary goal of this study is to investigate methods that can improve HCI through usercentric design. Interactive systems can be made more user-friendly, interesting, and productive if we put the user's needs, wants, and habits at the forefront of the design process (Cano et al, 2022).…”

Section: Introductionmentioning

confidence: 99%

Human-Computer Interaction: Enhancing User Experience in Interactive Systems

Kheder

2023

kje

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In this research, we investigate how human-computer interaction (HCI) can be used to improve the user experience (UX) of interactive systems. Studies in cognitive psychology, information processing, and human factors are examined as they relate to the development of HCI. It highlights how HCI has shifted its focus from functionality to user-friendliness, teaching ability, efficiency, enjoyment, and emotion. To better understand the current state of HCI and UX research, practice, and theory, a systematic literature study is performed. Focusing on users' goals wants, and characteristics at every stage of the design process is central to user-centered design (UCD) ideas and approaches, which are discussed at length in this article. We investigate usability testing as a crucial technique for bettering HCI, focusing on its advantages in pinpointing usability problems, boosting system efficacy, and boosting user pleasure. Methods for creating tests, finding participants, collecting data, and analyzing results are discussed. The importance of prototype methods in HCI and user-centric design is also emphasized in the study. This article delves into the practice of creating prototypes to collect user feedback, iterate designs, and perfect interactive systems. Techniques covered include paper prototyping, interactive wireframes, and high-fidelity prototypes. We propose interaction design frameworks like the User-Centered Design Process (UCDP) and the Double Diamond model to help designers prioritize users when developing interactive systems. The study also delves into how technologies like augmented reality, virtual reality, natural language processing, machine learning, and gesture-based interfaces have revolutionized HCI in recent years. The paper defends user-centric design's place in HCI, pointing out how UX affects user happiness, participation, and output. Researchers and practitioners in HCI and software engineering can greatly benefit from this paper's findings.

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Using Brain -Computer Interface to evaluate the User eXperience in interactive systems

Cited by 5 publications

References 34 publications

Modelling and Measuring Trust in Human–Robot Collaboration

Modelling and Measuring Trust in Human–Robot Collaboration

Inspiring Real-Time Evaluation and Optimization of Human–Robot Interaction with Psychological Findings from Human–Human Interaction

Human-Computer Interaction: Enhancing User Experience in Interactive Systems

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