An Adaptive Control Approach for Intelligent Wheelchair Based on BCI Combining with QoO

Fei Wang, Zongfeng Xu, Weiwei Zhang, Shichao Wu, Yahui Zhang, Sonya Coleman

    Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

    Abstract

    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.

    Original languageEnglish
    Title of host publication2020 International Joint Conference on Neural Networks, IJCNN 2020 - Proceedings
    PublisherInstitute of Electrical and Electronics Engineers Inc.
    ISBN (Electronic)9781728169262
    DOIs
    Publication statusPublished - 28 Sep 2020
    Event2020 International Joint Conference on Neural Networks, IJCNN 2020 - Virtual, Glasgow, United Kingdom
    Duration: 19 Jul 202024 Jul 2020

    Publication series

    NameInternational Joint Conference on Neural Networks (IJCNN)
    PublisherIEEE
    ISSN (Print)2161-4393
    ISSN (Electronic)2161-4407

    Conference

    Conference2020 International Joint Conference on Neural Networks, IJCNN 2020
    CountryUnited Kingdom
    CityVirtual, Glasgow
    Period19/07/2024/07/20

    Keywords

    • adaptive control
    • BCI
    • brain-controlled
    • intelligent wheelchair
    • Quality of Operating

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