@article{scholars13830,
       publisher = {Compusoft},
         journal = {Compusoft},
           pages = {3606--3610},
            year = {2020},
           title = {Shredded control of drones via motor imagery brain computer interface},
          number = {3},
          volume = {9},
            note = {cited By 2},
        abstract = {In this paper, we present architecture for an auxiliary shredded control that can be used in combination with a BCI control system. BCI systems are known for low reliability and accuracy. We are presenting a method to enhance this inherent weakness of BCI systems, by providing an assistive autonomous controller whenever the BCI system reaches to the point where fine control is necessary. The course control is performed by the motor imagery BCI which decodes the thinking process of the user and use it for navigating a quadcopter drone. Once the quadcopter reaches an area where the object selected to be picked up enters in the field of view of the bottom camera of the quadcopter, the autonomous controller is taking over the fine movement navigation to handle the rest of the task. This method is an exciting opportunity for several other BCI applications to enhance the reliability and accuracy of BCI systems to be adopted in everyday life of severely disabled patients. {\^A}{\copyright} National Institute of Science Communication and Information Resources (NISCAIR).},
             url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85087273987&partnerID=40&md5=1021b095c54611c9a4272fc74c3789de},
            issn = {23200790},
          author = {Alwasiti, H. and Yusoff, M. Z.}
}