Publications by authors named "Nida Itrat Abbasi"

During the last decade, children have shown an increasing need for mental wellbeing interventions due to their anxiety and depression issues, which the COVID-19 pandemic has exacerbated. Socially Assistive Robotics have been shown to have a great potential to support children with mental wellbeing-related issues. However, understanding how robots can be used to aid the measurement of these issues is still an open challenge.

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The field of human-robot interaction (HRI) research is multidisciplinary and requires researchers to understand diverse fields including computer science, engineering, informatics, philosophy, psychology, and more disciplines. However, it is hard to be an expert in everything. To help HRI researchers develop methodological skills, especially in areas that are relatively new to them, we conducted a virtual workshop, Workshop Your Study Design (WYSD), at the 2021 International Conference on HRI.

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Understanding the human brain's perception of different thermal sensations has sparked the interest of many neuroscientists. The identification of distinct brain patterns when processing thermal stimuli has several clinical applications, such as phantom-limb pain prediction, as well as increasing the sense of embodiment when interacting with neurorehabilitation devices. Notwithstanding the remarkable number of studies that have touched upon this research topic, understanding how the human brain processes different thermal stimuli has remained elusive.

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Decoding olfactory cognition has been generating significant interest in recent years due to a wide range of applications, from diagnosing neurodegenerative disorders to consumer research and traditional medicine. In this study, we have investigated whether changes in odor stimuli evaluation across repeated stimuli presentation can be attributed to changes in brain perception of the stimuli. Epoch intervals representing olfactory sensory perception were extracted from electroencephalography (EEG) signals using minimum variance distortionless response (MVDR)-based single trial event related potential (ERP) approach to understand the evoked response to high pleasantness and low pleasantness stimuli.

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Olfactory perception involves complex processing distributed along several cortical and sub-cortical regions in the brain. Although several studies have shown that the power spectra of the electroencephalography (EEG) contain information that can be used to differentiate between pleasant and unpleasant stimuli, there are still no studies which investigate whether EEG can be used to differentiate between the neural responses to olfactory stimuli of different levels of pleasantness. For this purpose, in the present study, local brain information within established frequency bands (θ, α and γ) has been used to devise discriminative features in a classification approach.

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Development of techniques for detection of mental fatigue has varied applications in areas where sustaining attention is of critical importance like security and transportation. The objective of this study is to develop a novel real-time driving fatigue detection methodology based on dry Electroencephalographic (EEG) signals. The study has employed two methods in the online detection of mental fatigue: power spectrum density () and sample entropy (SE).

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Development of interventions to prevent vigilance decrement has important applications in sensitive areas like transportation and defence. The objective of this work is to use multisensory (visual and haptic) stimuli for cognitive enhancement during mundane tasks. Two different epoch intervals representing sensory perception and motor response were analysed using minimum variance distortionless response (MVDR) based single trial ERP estimation to understand the performance dependency on both factors.

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