Biography
Mohamad Eid is an associate professor of electrical engineering and the director of the Applied Interactive Multimedia research lab (AIMLab) at New York University Abu Dhabi, UAE. He received the PhD in Electrical and Computer Engineering from the University of Ottawa, Canada, in 2010. He was previously a teaching and research associate at the University of Ottawa from June 2008 until April 2012. He is the co-author of the book: “Haptics Technologies: Bringing Touch to Multimedia”, Springers 2011. He is the recipient of several best paper award in several international conferences such as ICBAE 2016, DS-RT 2008 and the ACM Multimedia 2009 Grand Challenge Most Entertaining Award. He has more than 130 conference and journal publications and 5 patents. He is an editor at the IEEE Transactions on Haptics journal and a guest editor in several journals related to haptics. His
academic interests include affective haptics, neurohaptics, and novel haptic interfaces.
Talk: Modulating Perceived Urgency Using Vibrotactile Stimulation: Neural Perspective
Abstract:
Notification systems that elicit a desirable level of urgency are critical in human-computer interaction. Haptic feedback systems have the potential to modulate the perceived urgency without affecting the cognitive load in a wide spectrum of applications such as in-vehicle, mobile phone alerting systems, interaction with impaired individuals, and battlefield awareness for soldiers. In this presentation, we examine EEG correlates associated with the perceived urgency elicited by vibration stimulation at the upper body. Two vibration patterns are developed to elicit two levels of urgency (urgent and very urgent). Neural analysis revealed that the power spectral density of the delta, theta, and alpha frequency bands in the middle central area (C1, Cz, and C2) significantly increased for the urgent vibration and the very urgent vibration conditions as compared to the no vibration condition. Furthermore, significant differences in theta frequency band is observed in the prefrontal area between the three conditions. A machine learning model is developed to classify, based on single trial EEG data, the level of urgency and explainability analysis was utilized to cross-validate the results obtained in the neural analysis.
