University of Illinois Urbana-Champaign

EEG-based brain-computer interface for human-robot collaboration

Li, Yao

Content Files
LI-DISSERTATION-2020.pdf

Permalink

https://hdl.handle.net/2142/109363

Description

Title
EEG-based brain-computer interface for human-robot collaboration
Author(s)
Li, Yao
Issue Date
2020-11-19
Director of Research (if dissertation) or Advisor (if thesis)
Kesavadas, Thenkurussi
Doctoral Committee Chair(s)
Kesavadas, Thenkurussi
Committee Member(s)
  • Reis, Henrique M
  • Sreenivas, Ramavarapu S
  • Chowdhary, Girish
Department of Study
Industrial&Enterprise Sys Eng
Discipline
Systems & Entrepreneurial Engr
Degree Granting Institution
University of Illinois at Urbana-Champaign
Degree Name
Ph.D.
Degree Level
Dissertation
Date of Ingest
2021-03-05T21:36:58Z
Keyword(s)
  • BCI
  • EEG
  • SSVEP
  • Industrial robot
Abstract
One of the expectations for the next-generation industrial robots is to work collaboratively with humans. Collaborative robots must be able to communicate with human collaborators intelligently and seamlessly. However, industrial robots in prevalence are not good at understanding human intentions and decisions. We propose to develop human-robot interactions based on Brain-Computer Interfaces (BCIs) transferring human cognition to robots directly. By collecting and encoding brain activities with BCIs, human can actively send commands to robots in thought or passively let robots monitor mental activities. We conduct two major experiments, i.e. BCI for welding robot and BCI for defective part picking robot, through which human operators can actively communicate with robots and work collaboratively on manufacturing tasks. The BCI for welding robot allows operators to select weld beads and command the robot to weld in thought. In the picking robot study, the robot picks defective part from a conveyor based on the decisions made when operators examining the qualities visually. Besides, to build faster and more accurate BCIs, we propose a Conv-CA model, which combines convolutional neural network (CNN) and canonical correlation analysis (CCA) to improve the performance of the state-of-art steady-state visually evoked potential (SSVEP) algorithm. We also conduct a study for passive BCI communication, i.e. the robot detects the circumstance when operators feel unsafe in the human-robot collaboration. When a fear response is detected, the robot can stop immediately to protect human safety.
Graduation Semester
2020-12
Type of Resource
Thesis
Permalink
http://hdl.handle.net/2142/109363
Copyright and License Information
Copyright 2020 Yao Li

Owning Collections

Graduate Dissertations and Theses at Illinois PRIMARY
Graduate Theses and Dissertations at Illinois
Dissertations and Theses - Industrial and Enterprise Systems Engineering