TY - GEN
T1 - EEG Controlled Automated Writing Robotic Arm Based on Steady State Visually Evoked Potential
AU - Zhu, Xuequan
AU - Mu, Meng
AU - Belkacem, Abdelkader Nasreddine
AU - Shin, Duk
AU - Xu, Rui
AU - Wang, Kun
AU - Wang, Zhongpeng
AU - Wang, Changming
AU - Chen, Chao
N1 - Publisher Copyright:
© 2019 IEEE.
PY - 2019/6
Y1 - 2019/6
N2 - Brain-Computer Interface(BCI) refers to devices that allow people to communicate or control the outside devices only through brain waves without relying on their own output pathways, such as the human nervous system and muscle tissue. The problem of insufficient information transfer and interaction between organism and electromechanical device in electromechanical integration system is pointed out. This paper uses BP(Brain Products) equipment to collect Steady State Visual Evoked Potential (SSVEP) signals. The collected SSVEP signals were preprocessed, feature extracted and feature classified. Then it is connected with the robot arm to build a portable brain-computer interface control system. Six subjects participated in the online experiment of the system. Experimental results show that the system can write some simple Chinese characters with high accuracy, and the system is feasible and effective. Then the signal is taken by Open Brain-computer Interface (OpenBCI) to complete the connection with the robotic arm. We will realize the control of the robotic arm in the later experiment. Our research aim is to find an relatively effective control method by comparing BP and OpenBCI based control on the robotic arm.
AB - Brain-Computer Interface(BCI) refers to devices that allow people to communicate or control the outside devices only through brain waves without relying on their own output pathways, such as the human nervous system and muscle tissue. The problem of insufficient information transfer and interaction between organism and electromechanical device in electromechanical integration system is pointed out. This paper uses BP(Brain Products) equipment to collect Steady State Visual Evoked Potential (SSVEP) signals. The collected SSVEP signals were preprocessed, feature extracted and feature classified. Then it is connected with the robot arm to build a portable brain-computer interface control system. Six subjects participated in the online experiment of the system. Experimental results show that the system can write some simple Chinese characters with high accuracy, and the system is feasible and effective. Then the signal is taken by Open Brain-computer Interface (OpenBCI) to complete the connection with the robotic arm. We will realize the control of the robotic arm in the later experiment. Our research aim is to find an relatively effective control method by comparing BP and OpenBCI based control on the robotic arm.
KW - Brain-Computer Interface
KW - Electroencephalography (EEG)
KW - Robotic arm
KW - SSVEP
UR - http://www.scopus.com/inward/record.url?scp=85084676902&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85084676902&partnerID=8YFLogxK
U2 - 10.1109/CIVEMSA45640.2019.9071613
DO - 10.1109/CIVEMSA45640.2019.9071613
M3 - Conference contribution
AN - SCOPUS:85084676902
T3 - 2019 IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications, CIVEMSA 2019 - Proceedings
BT - 2019 IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications, CIVEMSA 2019 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 24th Annual IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications, CIVEMSA 2019
Y2 - 14 June 2019 through 16 June 2019
ER -