  In our research, we attempt to help people recognize their brain state of
concentration or relaxation more conveniently and in real time. Considering the
inconvenience of wearing traditional multiple electrode electroencephalographs,
we choose Muse to collect data which is a portable headband launched lately
with a number of useful functions and channels and it is much easier for the
public to use. Besides, traditional online analysis did not focus on the
synchronism between users and computers and the time delay problem did exist.
To solve the problem, by building the Analytic Hierarchy Model, we choose the
two gamma wave channels of F7 and F8 as the data source instead of using both
beta and alpha channels traditionally.Using the Common Space Pattern algorithm
and the Support Vector Machine model, the channels we choose have a higher
recognition accuracy rate and smaller amount of data to be dealt with by the
computer than the traditional ones. Furthermore, we make use of the Feedforward
Neural Network Model to predict subjects'brain states in half a second.
Finally, we design a plane program in Python where a plane can be controlled to
go up or down when users concentrate or relax. The SVM model and the
Feedforward Neural Network model have both been tested by 12 subjects and they
give an evaluation ranging from 1 to 10 points. The former gets 7.58 points
while the latter gets 8.83, which proves that the time delay problem is
improved once more.
