Abstract:
Brain-computer interfacing (BCI) is a technology that is almost four decades old and
it was developed solely for the purpose of developing and enhancing the impact of
Neuroprosthetics. As the non-invasive EEG headsets are made and used for
commercialization there are lot of application has seen such as home automation,
wheelchair control, controlling vehicle steering etc.
Controlling the drone with brain is one the latest application developed with the help
ofBCI. These applications, however, do not require a very high-speed response and
give satisfactory results when standard classification methods like Support Vector
Machine (SVM) and Multi-Layer Perceptron (MLPC). Issues are raised when there is
a high-speed control requirement for fixed-wing unmanned aerial operation. Vehicles
where such methods are kept unstable due to the low rate of classification. Such an
application requires the system to classify data at high speeds in order to retain the
controllability of the vehicle. This paper proposes a novel method of classification
which uses a combination of Common Spatial Paradigm and Linear Discriminant
Analysis that provides an improved classification accuracy in real time. A non-linear
SVM based classification technique has also been discussed. Further, this paper
discusses the implementation of the proposed method on a fixed-wing and VTOL
unmanned aerial vehicles.
