TY - GEN
T1 - An Adaptive Control Approach for Intelligent Wheelchair Based on BCI Combining with QoO
AU - Wang, Fei
AU - Xu, Zongfeng
AU - Zhang, Weiwei
AU - Wu, Shichao
AU - Zhang, Yahui
AU - Coleman, Sonya
N1 - Funding Information:
This work was supported in part by Natural Science Foundation of China under Grant 61973065, the Fundamental Research Funds for the Central Universities of China under Grant N172608005 and N182612002, Liaoning Provincial Natural Science Foundation of China under Grant 20180520007.
Publisher Copyright:
© 2020 IEEE.
Copyright:
Copyright 2020 Elsevier B.V., All rights reserved.
PY - 2020/9/28
Y1 - 2020/9/28
N2 - In recent years, brain-controlled intelligent wheelchairs have received extensive attention, which combines the accessibility of the Brain-computer Interface (BCI) system with the intelligence of wheelchairs. However, current brain-controlled wheelchairs are always operated in a fixed mode. The Electroencephalogram (EEG) signals with the fixed acquisition time are analyzed without considering the state of the user, which not only increases the risk of misoperation, but seriously reduces the information transfer rate of the system. To solve this problem, an adaptive control approach for intelligent wheelchair based on BCI combining with Quality of Operating (QoO) is proposed. Firstly, the influence of motor imagery signals with different time lengths in different states on classification accuracy was analyzed using tangent space Support Vector Machine (TSSVM) algorithm. Then, the definition of QoO was introduced, which was obtained by analyzing sample entropy and power spectral density (PSD) of four kinds of EEG activities, delta, theta, alpha and beta. Finally, the acquisition time of required EEG signals was adjusted according to the value of QoO. We constructed a brain-controlled wheelchair system and conducted real environmental experiments for 9 subjects using strategies, with and without adaptive control approach. The results show that the approach proposed in this paper can reduce the risk of misoperation and increase the information transfer rate on the premise of ensuring the classification performance during navigation in complex indoor environment.
AB - In recent years, brain-controlled intelligent wheelchairs have received extensive attention, which combines the accessibility of the Brain-computer Interface (BCI) system with the intelligence of wheelchairs. However, current brain-controlled wheelchairs are always operated in a fixed mode. The Electroencephalogram (EEG) signals with the fixed acquisition time are analyzed without considering the state of the user, which not only increases the risk of misoperation, but seriously reduces the information transfer rate of the system. To solve this problem, an adaptive control approach for intelligent wheelchair based on BCI combining with Quality of Operating (QoO) is proposed. Firstly, the influence of motor imagery signals with different time lengths in different states on classification accuracy was analyzed using tangent space Support Vector Machine (TSSVM) algorithm. Then, the definition of QoO was introduced, which was obtained by analyzing sample entropy and power spectral density (PSD) of four kinds of EEG activities, delta, theta, alpha and beta. Finally, the acquisition time of required EEG signals was adjusted according to the value of QoO. We constructed a brain-controlled wheelchair system and conducted real environmental experiments for 9 subjects using strategies, with and without adaptive control approach. The results show that the approach proposed in this paper can reduce the risk of misoperation and increase the information transfer rate on the premise of ensuring the classification performance during navigation in complex indoor environment.
KW - adaptive control
KW - BCI
KW - brain-controlled
KW - intelligent wheelchair
KW - Quality of Operating
UR - http://www.scopus.com/inward/record.url?scp=85093826323&partnerID=8YFLogxK
U2 - 10.1109/IJCNN48605.2020.9207175
DO - 10.1109/IJCNN48605.2020.9207175
M3 - Conference contribution
AN - SCOPUS:85093826323
T3 - International Joint Conference on Neural Networks (IJCNN)
BT - 2020 International Joint Conference on Neural Networks, IJCNN 2020 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2020 International Joint Conference on Neural Networks, IJCNN 2020
Y2 - 19 July 2020 through 24 July 2020
ER -