17 citations · 30 across the 8 of their papers we have counts for
10 papers
Gamification of Motor Imagery Brain-Computer Interface Training Protocols: a systematic review
Fred Atilla, Marie Postma, Maryam Alimardani
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…
Differentiating Workload using Pilot's Stick Input in a Virtual Reality Flight Task
Evy van Weelden, Carl W. E. van Beek, Maryam Alimardani +4
High-risk operational tasks such as those in aviation require training environments that are realistic and capable of inducing high levels of workload. Virtual Reality (VR) offers…
Investigating the Impact of a Dual Musical Brain-Computer Interface on Interpersonal Synchrony: A Pilot Study
Anita Vrins, Ethel Pruss, Caterina Ceccato +2
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 compare…
End-to-End Deep Transfer Learning for Calibration-free Motor Imagery Brain Computer Interfaces
Maryam Alimardani, Steven Kocken, Nikki Leeuwis
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 calibrat…
Cognitive Workload Associated with Different Conceptual Modeling Approaches in Information Systems
Andreas Knoben, Maryam Alimardani, Arash Saghafi +1
Conceptual models visually represent entities and relationships between them in an information system. Effective conceptual models should be simple while communicating sufficient i…
EEG-based Classification of Drivers Attention using Convolutional Neural Network
Fred Atilla, Maryam Alimardani
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 stud…
High Aptitude Motor Imagery BCI Users Have Better Visuospatial Memory
Nikki Leeuwis, Maryam Alimardani
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 BC…
Robot-Assisted Mindfulness Practice: Analysis of Neurophysiological Responses and Affective State Change
Maryam Alimardani, Linda Kemmeren, Kazuki Okumura +1
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 tha…
Prediction of Human Empathy based on EEG Cortical Asymmetry
Andrea Kuijt, Maryam Alimardani
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 di…
Assessment of Empathy in an Affective VR Environment using EEG Signals
Maryam Alimardani, Annabella Hermans, Angelica M. Tinga
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 thei…