Stimulus design, EEG experimentation, and signal processing for a brain computer interface: Target marking and tracking in videos

2022-2023 Fall
Faculty Department of Project Supervisor: 
Faculty of Engineering and Natural Sciences
Number of Students: 

Computer vision-based multi-target tracking fails in especially unmanned aerial vehicle (UAV) videos due to several reasons such as occlusion and low resolution.This project aims to be able to continue tracking, when the computer vision-based tracking fails in UAV videos, by using a brain-computer interface (BCI). Hence, our goal is to develop a hybrid tracker utilizing computer vision and BCI together. The BCI component will be an end-to-end video target marking/tracking electroencephalography (EEG) system, where the stimulation is based on Steady-State Visual Evoked Potentials (SSVEP). The planned work includes 1) the stimuli design, 2) EEG experiments, 3) algorithm design and 4) performance analyses.
We are currently in the second phase. Therefore the PURE students will start working on the EEG experiments first (in particular, participating the EEG experiments and the pre-processing of the EEG signals and preparing them as inputs to machine learning), and then can continue to the very end if they wish.

Related Areas of Project: 
Computer Science and Engineering
Electronics Engineering
Mechatronics Engineering

About Project Supervisors

EE Faculty Member, Hüseyin Özkan (
Psychology Faculty Member, Nihan Alp (