Link to the University of Pittsburgh Homepage
Link to the University Library System Homepage Link to the Contact Us Form

EEG-guided Closed Loop Electro-tactile Model for Haptics Applications

Eldeeb, Safaa (2022) EEG-guided Closed Loop Electro-tactile Model for Haptics Applications. Doctoral Dissertation, University of Pittsburgh. (Unpublished)

[img] PDF
Primary Text
Restricted to University of Pittsburgh users only until 6 September 2024.

Download (17MB) | Request a Copy


Haptic feedback is essential for daily activities, since it provides us with the ability to become aware of our surroundings. Modern approaches that provide haptic feedback focus mainly on robotic manipulators, vibrators, and tactors. This type of feedback tends to be cumbersome and limited to a small number of contact points. On the contrary, electro-tactile displays are compact, portable, non expensive and wearable, and recent discoveries demonstrate that naturalistic sensations of touch can be provided by electrical stimulation of peripheral nerves. The long-term goal of this work is to enhance sensory perception in artificial interfaces for various applications including neuroprothestics, teleoperation, surgical training of physicians in virtual environment and providing tactile feedback for video-game users in virtual
reality environments.
The direct goal of this work is to develop techniques to identify and extract EEG features that are markers for real world haptic interactions. Moreover, develop a closed-loop system guided by EEG to control sensory stimulation for enhanced spatial presence.
Even though a number of methods have been developed to measure perception of spatial presence and provide sensory feedback in virtual reality environments, there is currently no closed-loop control of sensory stimulation that couple the spatial presence measures to adaptive adjustment of stimulation levels. More specifically, in this work we aim to develop an EEG-guided closed loop electro-tactile stimulation model that adaptively improve haptic presence in virtual environments and for neuroprothestics.


Social Networking:
Share |


Item Type: University of Pittsburgh ETD
Status: Unpublished
CreatorsEmailPitt UsernameORCID
Eldeeb, Safaasme46@pitt.edusme460000-0002-5704-5408
ETD Committee:
TitleMemberEmail AddressPitt UsernameORCID
Committee ChairAkcakaya, Muratakcakaya@pitt.eduakcakaya0000-0001-5094-1931
Committee MemberEl-Jaroudi, AmroAmro@pitt.eduAmro
Committee MemberMiskov-Zivanov, Natasanmzivanov@pitt.edunmzivanov
Committee MemberSejdic, Ervinesejdic@pitt.eduesejdic0000-0003-4987-8298
Committee MemberErdogmus,
Date: 6 September 2022
Date Type: Publication
Defense Date: 2 June 2022
Approval Date: 6 September 2022
Submission Date: 9 June 2022
Access Restriction: 2 year -- Restrict access to University of Pittsburgh for a period of 2 years.
Number of Pages: 150
Institution: University of Pittsburgh
Schools and Programs: Swanson School of Engineering > Electrical and Computer Engineering
Degree: PhD - Doctor of Philosophy
Thesis Type: Doctoral Dissertation
Refereed: Yes
Uncontrolled Keywords: EEG, Haptic, Touch, Deep Learning, System Identification, machine learning
Date Deposited: 06 Sep 2022 16:24
Last Modified: 06 Sep 2022 16:24


Monthly Views for the past 3 years

Plum Analytics

Actions (login required)

View Item View Item