Motor Imagery Brain-Computer Interface for RPAS Command and Control

被引:0
|
作者
Arnaldo, Rosa [1 ]
Gomez Comendador, Fernando [1 ]
Perez, Luis [1 ]
Rodriguez, Alvaro [1 ]
机构
[1] Univ Politecn Madrid, Sch Aeronaut & Space Engn, Plaza Cardenal Cisneros 3, E-28040 Madrid, Spain
关键词
Brain Computer Interface; Motor system; Signal processing; EEG; COMMUNICATION;
D O I
10.1007/978-3-319-60366-7_31
中图分类号
V [航空、航天];
学科分类号
08 ; 0825 ;
摘要
Nowadays, technology is evolving towards the development of new controlling methods based in signals produced by our brain (Brain Computer Interfaces BCI). Applications of Brian computing Interfaces are also being explored in the field of aeronautics. This paper presents the initial steps of a work focused on the evaluation of brain patterns that occur when an individual excites the brain to perform an action. The final goal of this project is to implement this method in a real time program that is capable of filtering the signals obtained by brain measurement system; treating these signals to obtain the amplitude accumulation at the indicated frequencies and sending the control commands to a RPAS in order to be able to control it.
引用
收藏
页码:325 / 335
页数:11
相关论文
共 50 条
  • [41] Hybrid brain-computer interface with motor imagery and error-related brain activity
    Mousavi, Mahta
    Krol, Laurens R.
    de Sa, Virginia R.
    [J]. JOURNAL OF NEURAL ENGINEERING, 2020, 17 (05)
  • [42] Signal processing algorithms for motor imagery brain-computer interface:State of the art
    Hong, Jie
    Qin, Xiansheng
    Li, Jing
    Niu, Junlong
    Wang, Wenjie
    [J]. JOURNAL OF INTELLIGENT & FUZZY SYSTEMS, 2018, 35 (06) : 6405 - 6419
  • [43] Unsupervised Processing Methods for Motor Imagery-Based Brain-Computer Interface
    Eldeib, Ayman M.
    Sarhan, Ola
    Wahed, Manal Abdel
    [J]. 2018 IEEE 4TH MIDDLE EAST CONFERENCE ON BIOMEDICAL ENGINEERING (MECBME), 2018, : 106 - 111
  • [44] Multimodal feedback in assisting a wearable brain-computer interface based on motor imagery
    Arpaia, Pasquale
    Coyle, Damien
    Donnarumma, Francesco
    Esposito, Antonio
    Natalizio, Angela
    Parvis, Marco
    Pesola, Marisa
    Vallefuoco, Ersilia
    [J]. 2022 IEEE INTERNATIONAL CONFERENCE ON METROLOGY FOR EXTENDED REALITY, ARTIFICIAL INTELLIGENCE AND NEURAL ENGINEERING (METROXRAINE), 2022, : 691 - 696
  • [45] Motor Imagery-based Brain-Computer Interface: Neural Network Approach
    Lazurenko, D. M.
    Kiroy, V. N.
    Shepelev, I. E.
    Podladchikova, L. N.
    [J]. OPTICAL MEMORY AND NEURAL NETWORKS, 2019, 28 (02) : 109 - 117
  • [46] On the Assessment of Functional Connectivity in an Immersive Brain-Computer Interface During Motor Imagery
    Alanis-Espinosa, Myriam
    Gutierrez, David
    [J]. FRONTIERS IN PSYCHOLOGY, 2020, 11
  • [47] Feature fusion for improving performance of motor imagery brain-computer interface system
    Radman, Moein
    Chaibakhsh, Ali
    Nariman-zadeh, Nader
    He, Huiguang
    [J]. BIOMEDICAL SIGNAL PROCESSING AND CONTROL, 2021, 68
  • [48] Designing discriminative spatial filter vectors in motor imagery brain-computer interface
    Lee, Kyeong-Yeon
    Kim, Sun
    [J]. INTERNATIONAL JOURNAL OF IMAGING SYSTEMS AND TECHNOLOGY, 2013, 23 (02) : 147 - 151
  • [49] Serious Game for Motor-Imagery based Brain-Computer Interface training
    Ianosi-Andreeva-Dimitrova, Alexandru
    Mandru, Silviu-Dan
    [J]. 2021 INTERNATIONAL CONFERENCE ON E-HEALTH AND BIOENGINEERING (EHB 2021), 9TH EDITION, 2021,
  • [50] Pattern Recognition of Motor Imagery EEG Signal in Noninvasive Brain-Computer Interface
    Qu, Shen
    Liu, Jingmeng
    Chen, Weihai
    Zhang, Jianbin
    Chen, Weidong
    [J]. PROCEEDINGS OF THE 2018 13TH IEEE CONFERENCE ON INDUSTRIAL ELECTRONICS AND APPLICATIONS (ICIEA 2018), 2018, : 1814 - 1819