Maryam Alimardani

• Doctor of Engineering
• Professor (Associate) at Vrije Universiteit Amsterdam

Trust is a fundamental component of human-agent interaction. With the increasing presence of artificial agents in daily life, it is essential to understand how people perceive and trust these agents. One of the key challenges affecting this perception is the Uncanny Valley Effect (UVE), where increasingly human-like artificial beings can be perceiv...

Effective cognitive workload management has a major impact on the safety and performance of pilots. Integrating brain-computer interfaces (BCIs) presents an opportunity for real-time workload assessment. Leveraging cognitive workload data from immersive, high-fidelity virtual reality (VR) flight simulations enhances ecological validity and allows f...

Anthropomorphic agents are generally evaluated more positively and trustworthy by human users than agents that are not humanlike. However, subtle mismatches in an agent’s appearance and behavior can lead to perceived uncanniness resulting in a disrupted trust during human-agent interaction. This study investigated the impact of an agent’s appearanc...

Current Motor Imagery Brain-Computer Interfaces (MI-BCI) require a lengthy and monotonous training procedure to train both the system and the user. Considering many users struggle with effective control of MI-BCI systems, a more user-centered approach to training might help motivate users and facilitate learning, alleviating inefficiency of the BCI...

Current Motor Imagery Brain-Computer Interfaces (MI-BCI) require a lengthy and monotonous training procedure to train both the system and the user. Considering many users struggle with effective control of MI-BCI systems, a more user-centered approach to training might help motivate users and facilitate learning, alleviating inefficiency of the BCI...

The current study investigated the effectiveness of social robots in facilitating stress management interventions for university students by evaluating their physiological responses. We collected electroencephalogram (EEG) brain activity and Galvanic Skin Responses (GSRs) together with self-reported questionnaires from two groups of students who pr...

Quantifying workload is necessary for effective and personalized flight training of student pilots: their workload must not be too low (risk of boredom) nor too high (overload). Passive brain-computer interfaces (pBCIs) allow for measurement of an individual's workload from their brain activity, however, the performance of pBCIs remains sub-optimal...

The utmost issue in Motor Imagery Brain-Computer Interfaces (MI-BCI) is the BCI poor performance known as ‘BCI inefficiency’. Although past research has attempted to find a solution by investigating factors influencing users’ MI-BCI performance, the issue persists. One of the factors that has been studied in relation to MI-BCI performance is gender...

Preprint of extended abstract that is approved to the 2024 PsychoNeuroEconomics conference. In neuroeconomics and marketing studies, neural indicators of emotions are sought after to investigate the affective responses to communication stimuli. A commonly applied metric is Frontal Alpha Asymmetry (FAA), which refers to the lateralization of alpha-...

In neuroeconomics and marketing studies, neural indicators of emotions are sought after to investigate the affective responses to communication stimuli. A commonly applied metric is Frontal Alpha Asymmetry (FAA), which refers to the lateralization of alpha-band activity in the frontal brain region. The interpretation of this metric is still deliber...

Prospective memory (PM), the ability to remember to perform tasks in the future, is vital for maintaining functional independence in older adults. This paper introduces "Virtual Day", a novel Virtual Reality (VR) game designed to assess PM in an immersive and realistic environment. We report a feasibility study where Virtual Day was compared to its...

Introduction The necessity to promote pro-environmental behavior change in individuals and society is increasingly evident. This study aimed to investigate the effect of evaluative conditioning on consumers’ perception of product packaging. Methods We first produced two stimulus sets: one including images of supermarket products with different pac...

Human-robot interaction often employs the Wizard-of-Oz (WoZ) paradigm, where a human controls the robot. However, this approach has limitations, such as a lack of autonomy that impedes real-world applications. Large language models (LLMs) can replace WoZ in conversational tasks, such as brainstorming. We propose that, in such application domains, L...

Brain Computer Interfaces (BCIs) are intelligent systems that enable direct communication between the human brain and machines. While BCI systems are promising for future medical and non-medical applications, studies concerning their ethical considerations are growing. However, no previous study has examined how the public’s ethical perception of t...

Stress affects many students, leaving them vulnerable to burnout. Social robots can provide personalized and non-judgmental support for individuals to engage in behavioral and cognitive therapy. This study investigated the effectiveness of a robot-assisted stress management intervention in reducing stress among university students. In a between-sub...

The aim of the current study was to investigate children's brain responses to robot-assisted language learning. EEG brain signals were collected from 41 Japanese children who learned French vocabularies in two groups; half of the children learned new words from a social robot that narrated a story in French using animations on a computer screen (Ro...

Significant efforts have been made in the past decade to humanize both the form and function of social robots to increase their acceptance among humans. To this end, social robots have recently been combined with brain-computer interface (BCI) systems in an attempt to give them an understanding of human mental states, particularly emotions. However...

Brain Computer Interfaces (BCIs) are intelligent systems that enable direct communication between the human brain and machines. While BCI systems are promising for future medical and non-medical applications, studies concerning their ethical considerations are growing. However, no previous study has examined how the public's ethical perception of t...

This study looked into how effective a Musical Brain-Computer Interface (MBCI) can be in providing feedback about synchrony between two people. Using a double EEG setup, we compared two types of musical feedback; one that adapted in real-time based on the inter-brain synchrony between participants (Neuroadaptive condition), and another music that w...

A major issue in Motor Imagery Brain-Computer Interfaces (MI-BCIs) is their poor classification accuracy and the large amount of data that is required for subject-specific calibration. This makes BCIs less accessible to general users in out-of-the-lab applications. This study employed deep transfer learning for development of calibration-free subje...

This paper investigates the efficacy of a passive Brain-Computer Interface (BCI) in enabling a robot tutor to adaptively respond to a user's engagement level in real-time. The BCI system extracted EEG Engagement Index from the user's electroencephalography (EEG) signals as an indicator of engagement during Human-Robot Interaction (HRI). A within-su...

Parent and child have been shown to synchronize their behaviors and physiology during social interactions. This synchrony is an important marker of their relationship quality and subsequently the child’s social and emotional development. Therefore, understanding the factors that influence parent–child synchrony is an important undertaking. Using EE...

Motor Imagery Brain-Computer Interfaces (MI-BCI) decode brain patterns associated with motor intentions into control commands for a variety of applications, bypassing traditional motor inputs. To use these systems, the user must produce identifiable and stable MI patterns, which requires multiple training sessions in a lab. However, MI-BCI training...

A dual brain-computer interface (BCI) was developed to translate emotions and synchrony between two users into music. Using EEG signals of two individuals, the system generates live music note- by-note and controls musical parameters, such as pitch, intensity and interval. The users’ mean EEG amplitude determines the notes, and their emotional vale...

Virtual Reality (VR) environments are promising creativity support tools that can be designed to enhance factors related to the creative process such as immersion, engagement, and flow. However, it is still unclear how brain responses associated with creativity are influenced by VR. To address this gap, we collected EEG signals from 21 participants...

Most individuals are aware of the wastefulness of plastic packaging but do not know what sustainable packaging exactly is or choose not to buy it. In an effort to educate consumers about the sustainability of their products, the current study employed an evaluative conditioning approach in which sustainable and unsustainable products were paired wi...

Social robots have been extensively studied in educational settings for children and their positive impacts on children’s learning are reported. The aim of this study was to find out whether embodied educational technologies such as robot tutors can also yield similar results with adult learners. An experiment was conducted in a secondary education...

Adaptive robots have the potential to support the overloaded healthcare system by helping new stroke survivors learn about their conditions. However, current adaptive robots often fail to maintain users’ engagement during interactions. This study investigated the impact of an adaptive robot on Social Agency which has been proposed to influence enga...

Robots are becoming increasingly popular as a teaching aid in language learning. For language learning, which relies on inter-personal interactions and references to the physical world, an agent’s embodiment and ability to adapt to the student are both important factors. In this study, adaptive behavior and embodiment were combined in robot-assiste...

The Uncanny Valley (UV) theory predicts that imperfectly human-like artificial agents elicit negative reactions in perceivers. While to date most studies investigating the UV have been behavioral, there is a growing number of neuroscientific studies that hold the potential of shedding light on the automatic processes related to the UV. The current...

Robots are becoming increasingly popular as a teaching aid in language learning. For language learning, which relies on inter-personal interactions and references to the physical world, an agent's embodiment and ability to adapt to the student are both important factors. In this study, adaptive behavior and embodiment were combined in robot-assiste...

Virtual reality (VR) offers a training environment that promotes increased learning and performance. However, to what extent VR flight simulations offer increased performance compared to less-immersive simulators is not clear, and neither are their underlying cognitive aspects. In a within-subject experiment, we compared fight performance and subje...

This paper systematically reviews 20 years of publications (N = 54) on aviation and neurophysiology. The main goal is to provide an account of neurophysiological changes associated with flight training with the aim of identifying neurometrics indicative of pilot's flight training level and task relevant mental states, as well as to capture the curr...

Virtual faces have been found to be rated less human-like and remembered worse than photographic images of humans. What it is in virtual faces that yields reduced memory has so far remained unclear. The current study investigated face memory in the context of virtual agent faces and human faces, real and manipulated, considering two factors of pred...

Most consumers are aware that climate change is a growing problem and admit that action is needed. However, research shows that consumers’ behavior often does not conform to their value and orientations. This value-behavior gap is due to contextual factors such as price, product design, and social norms as well as individual factors such as persona...

Motor Imagery Brain-Computer Interfaces (MI-BCIs) are AI-driven systems that capture brain activity patterns associated with mental imagination of movement and convert them into commands for external devices. Traditionally, MI-BCIs operate on Machine Learning (ML) algorithms, which require extensive signal processing and feature engineering to extr...

Using a Brain Computer Interface (BCI), which monitors a user's attention and engagement during learning tasks, enables adaptation of pedagogical strategies for a personalized learning experience. In this paper, we present an EEG-based passive BCI system for real-time evaluation of user engagement during a language learning task. The EEG Engagement...

Social robots have been shown effective in pedagogical settings due to their embodiment and social behavior that can improve a learner's motivation and engagement. In this study, the impact of a social robot's motivational gestures in robot-assisted language learning (RALL) was investigated. Twenty-five university students participated in a languag...

Alzheimer’s disease (AD) is a brain disorder that is mainly characterized by a progressive degeneration of neurons in the brain and decline of cognitive abilities. This study compared an FFT-based spectral analysis against a functional connectivity analysis for the diagnosis of AD. Both quantitative methods were applied on an EEG dataset including...

Performance variation among users is a well-known issue in the Brain-Computer Interface (BCI) field. Although extensive research has been conducted, there is yet no clear agreement on the specific factors that influence the BCI performance. Using a large sample size of 54 subjects, this study investigated the impact of different demographic and psy...

Alzheimers disease (AD) is a brain disorder that is mainly characterized by a progressive degeneration of neurons in the brain, causing a decline in cognitive abilities and difficulties in engaging in day-to-day activities. This study compares an FFT-based spectral analysis against a functional connectivity analysis based on phase synchronization,...

The current study investigated how robot tutors influence brain activity during child-robot interaction (CRI) for learning of second language vocabulary. We gathered EEG signals from two groups of children; 1) Robot group (N=21) who listened to a storytelling social robot and learned French words, and 2) Display group (N=20) who listened to the sam...

The impact of human factors on climate change is unequivocal. While consumers are increasingly becoming aware of their environmental footprint, this is not sufficient: contextual factors such as pricing, convenience, and packaging play a role in consumers’ decision-making. This has created a gap between consumers’ attitudes and behavior, which call...

BCI-controlled smart homes enable people with severe motor disabilities to perform household activities, which would otherwise be inaccessible to them. In this paper, we present a proof of concept of an assistive robot with telepresence functionality inside a Virtual Reality (VR) smart home. Using live EEG data and a P300 Brain-Computer Interface (...

BCI inefficiency is one of the major challenges of Motor Imagery Brain-Computer Interfaces (MI-BCI). Past research suggests that certain cognitive skills and personality traits correlate with MI-BCI real-time performance. Other studies have examined sensorimotor rhythm changes (known as μ suppression) as a valuable indicator of successful execution...

Social robots are being increasingly used in the therapy of children with autism spectrum disorder (ASD). However, robot interaction is often designed by HRI researchers who are not fully familiar with cognitive challenges faced by children with autism. This study aimed to validate a social robot interaction designed for emotion recognition trainin...

Social robots are being increasingly employed for educational purposes, such as second language tutoring. Past studies in Child-Robot Interaction (CRI) have demonstrated the positive effect of an embodied agent on engagement and consequently learning of the children. However, these studies commonly use subjective or behavioral metrics of engagement...

Motor Imagery BCI systems have a high rate of users that are not capable of modulating their brain activity accurately enough to communicate with the system. Several studies have identified psychological, cognitive, and neurophysiological measures that might explain this MI-BCI inefficiency. Traditional research had focused on mu suppression in the...

BCI inefficiency is one of the major challenges of motor imagery brain-computer interfaces (MI-BCI). Past research suggests that certain cognitive skills and personality traits correlate with MI-BCI real-time performance. Other studies have examined sensorimotor rhythm changes (also known as mu suppression) as a valuable indicator of successful exe...

BCI inefficiency is one of the major challenges of motor imagery brain-computer interfaces (MI-BCI). Past research suggests that certain cognitive skills and personality traits correlate with MI-BCI real-time performance. Other studies have examined sensorimotor rhythm changes (also known as µ suppression) as a valuable indicator of successful exec...

Social robots are being increasingly employed for educational purposes, such as second language tutoring. Past studies in Child-Robot Interaction (CRI) have demonstrated the positive effect of an embodied agent on engagement and consequently learning of the children. However, these studies commonly use subjective or behavioral metrics of engagement...

Accurate detection of a drivers attention state can help develop assistive technologies that respond to unexpected hazards in real time and therefore improve road safety. This study compares the performance of several attention classifiers trained on participants brain activity. Participants performed a driving task in an immersive simulator where...

Accurate detection of a driver's attention state can help develop assistive technologies that respond to unexpected hazards in real time and therefore improve road safety. This study compares the performance of several attention classifiers trained on participants' brain activity. Participants performed a driving task in an immersive simulator wher...

Brain computer interfaces (BCIs) provide a direct communication pathway between humans and computers. There are three major BCI paradigms that are commonly employed: motor-imagery (MI), event-related potential (ERP), and steady-state visually evoked potential (SSVEP). In our study, we sought to expand this by focusing on "Inner Speech" paradigm usi...

Motor Imagery (MI) is a mental process by which an individual rehearses body movements without actually performing physical actions. Motor Imagery Brain-Computer Interfaces (MI-BCIs) are AI-driven systems that capture brain activity patterns associated with this mental process and convert them into commands for external devices. Traditionally, MI-B...

Social robots are being increasingly used in the therapy of children with autism spectrum disorder (ASD). However, robot interaction is often designed by HRI researchers who are not fully familiar with cognitive challenges faced by children with autism. This study aimed to validate a social robot interaction designed for emotion recognition trainin...

The present study investigates the applicability of deep learning methods in EEG neuromarketing prediction tasks, compared to traditional machine learning approaches. Neuroscientific methods have expanded research capabilities in marketing and created new insights into consumer behavior and decision making processes. Both machine learning and deep...

Brain-computer interfaces (BCIs) are communication bridges between a human brain and external world, enabling humans to interact with their environment without muscle intervention. Their functionality, therefore, depends on both the BCI system and the cognitive capacities of the user. Motor-imagery BCIs (MI-BCI) rely on the users’ mental imaginatio...

In this study, we examined the effect of mindfulness meditation facilitated by human-robot interaction (HRI) on brain activity. EEG signals were collected from two groups of participants; Meditation group who practiced mindfulness meditation with a social robot and Control group who only listened to a lecture by the robot. We compared brain functio...

Despite advancements in computer graphics and artificial intelligence, it remains unclear which aspects of intelligent virtual agents (IVAs) make them identifiable as human-like agents. In three experiments and a computational study, we investigated which specific facial features in static IVAs contribute to judging them human-like. In Experiment 1...

Sleep apnea is a potentially fatal disorder that causes frequent breathing pauses during sleep. Prior research has shown that monitoring of EEG signals during sleep can contribute to automatic detection of apnea events. However, a more comprehensive classification of specific apnea types and their severity is required for accurate clinical diagnosi...

People can feel engaged and attribute human-like traits when interacting with a social robot and reveal this unconsciously to observers. Studies have suggested that behavioral signals such as facial expressions, posture, speech and laughter play an important role in identifying engagement in Human-Robot Interaction (HRI), however the effect of thes...

We examined the factorial structure and validity of a Japanese version of the Parental Burnout Assessment, the PBA-J, with 1,500 Japanese parents. The Parental Burnout Assessment measures burnout using four dimensions: exhaustion in one's parental role, contrast in parental self, feelings of being fed up, and emotional distancing. Confir-matory fac...

Brain computer interfaces (BCI) decode the electrophysiological signals from the brain into an action that is carried out by a computer or robotic device. Motor imagery BCIs (MI BCI) rely on the user s imagination of bodily movements, however not all users can generate the brain activity needed to control MI BCI. This difference in MI BCI performan...

Brain-computer interfaces (BCIs) have long been seen as control interfaces that translate changes in brain activity, produced either by means of a volitional modulation or in response to an external stimulation. However, recent trends in the BCI and neurofeedback research highlight passive monitoring of a user's brain activity in order to estimate...

People can feel engaged and attribute human-like traits when interacting with a social robot and reveal this unconsciously to observers. Studies have suggested that behavioral signals such as facial expressions, posture, speech and laughter play an important role in identifying engagement in Human Robot Interaction (HRI), however the effect of thes...

Brain-computer interfaces (BCI) translate brain activity into an action that is carried out by a computer or robotic device. Motor-imagery BCIs (MI-BCI) rely on the user's imagination of bodily movements, however not all users can generate the brain activity needed to control MI-BCI. This difference in MI-BCI performance among novice users could be...

Mindfulness is the state of paying attention to the present moment on purpose and meditation is the technique to obtain this state. This study aims to develop a robot assistant that facilitates mindfulness training by means of a Brain-Computer Interface (BCI) system. To achieve this goal, we collected EEG signals from two groups of subjects engagin...

Humans constantly interact with digital devices that disregard their feelings. However, the synergy between human and technology can be strengthened if the technology is able to distinguish and react to human emotions. Models that rely on unconscious indications of human emotions, such as (neuro)physiological signals, hold promise in personalizatio...

Variation of information in the firing rate of neural population, as reflected in different frequency bands of electroencephalographic (EEG) time series, provides direct evidence for change in neural responses of the brain to hypnotic suggestibility. However, realization of an effective biomarker for spiking behaviour of neural population proves to...

With the advancements in social robotics and virtual avatars, it becomes increasingly important that these agents adapt their behavior to the mood, feelings and personality of their users. One such aspect of the user is empathy. Whereas many studies measure empathy through offline measures that are collected after empathic stimulation (e.g. post-ho...

Sociable robots are slowly entering domains such as education and healthcare. As we are exposing our youth and elderly to these new intelligent technologies, it is important to understand their perception and attitudes towards robots. This study investigates the differences between elderly and young adults in ascribing mind perception to a sociable...

Virtual agents can be powerful elements in virtual reality (VR) applications, as their influence on user expe-rience is governed by complex social mechanisms. Public speaking offers a relatively high-stakes situationthat involves interaction with virtual agents. We examined the effects of an ingroup versus outgroup virtualaudience on public speakin...

Multimodal data enables powerful methodological approaches to investigate social group interaction. This paper specifically focuses on dialogic moments , i. e., episodes of human communication with high mutual understanding. We present preliminary results of a pilot study, where we apply multimodal analysis of dialogic moments in the context of sto...

The main objective of this research was to gain insight in the attitude that groups of elderly and young students have towards social robots. A total of 52 participants (24 elderly vs. 28 students) took part in a short-term interaction with a humanoid social robot. In small groups of two to four people, they engaged in a conversation with a Nao rob...

Sociable robots are slowly entering domains such as education and healthcare. As we are exposing our youth and elderly to these new intelligent technologies, it is important to understand their perception and attitudes towards robots. This study investigates the differences between elderly and young adults in ascribing mind perception to a sociable...

Recent work suggests that machine and deep learning models are prone to EEG artifacts and have staggering performance drops when used to classify EEG signals rich of noise. This particularly affects real-time performance of EEG monitoring systems such as brain-computer interfaces, thus rendering their applications in uncontrolled environments usele...

Reliable predictions of the impact of natural hazards turning into a disaster is important for better targeting humanitarian response as well as for triggering early action. Open data and machine learning can be used to predict loss and damage to the houses and livelihoods of affected people. This research focuses on agricultural loss, more specifi...

The main objective of this research was to gain insight in the attitude that groups of elderly and young students have towards social robots. A total of 52 participants (24 elderly vs. 28 students) took part in a short-term interaction with a humanoid social robot. In small groups of two to four people, they engaged in a conversation with a Nao rob...

It is crucial that naturally-looking Embodied Conversational Agents (ECAs) display various verbal and non-verbal behaviors, including facial expressions. The generation of credible facial expressions has been approached by means of different methods, yet remains difficult because of the availability of naturalistic data. To infuse more variability...

People's responses to a hypnosis intervention is diverse and unpredictable. A system that predicts user's level of susceptibility from their electroencephalography (EEG) signals can be helpful in clinical hypnotherapy sessions. In this paper, we extracted differential entropy (DE) of the recorded EEG from two groups of subjects with high and low hy...

This patent relates to the design and implementation of a computational model that utilizes the EEG activity of human subjects' brain activity to detect the onset of the hypnotic state and to determine the human subjects' degree of suggestibility during the hypnosis session.

Parenting is a precious experience and also a very hard task, which could result in parental burnout for some parents. The present study sought to validate a Japanese version of the Parental Burnout Inventory (PBI-J) by replicating and extending the pioneering work of Roskam et al. (2017). We conducted a web survey (N = 1200) to first validate the...