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In our research, we attempt to help people recognize their brain state ofconcentration or relaxation more conveniently and in real time. Considering theinconvenience of wearing traditional multiple electrode electroencephalographs,we choose Muse to collect data which is a portable headband launched latelywith a number of useful functions and channels and it is much easier for thepublic to use. Besides, traditional online analysis did not focus on thesynchronism between users and computers and the time delay problem did exist.To solve the problem, by building the Analytic Hierarchy Model, we choose thetwo gamma wave channels of F7 and F8 as the data source instead of using bothbeta and alpha channels traditionally.Using the Common Space Pattern algorithmand the Support Vector Machine model, the channels we choose have a higherrecognition accuracy rate and smaller amount of data to be dealt with by thecomputer than the traditional ones. Furthermore, we make use of the FeedforwardNeural 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 togo up or down when users concentrate or relax. The SVM model and theFeedforward Neural Network model have both been tested by 12 subjects and theygive an evaluation ranging from 1 to 10 points. The former gets 7.58 pointswhile the latter gets 8.83, which proves that the time delay problem isimproved once more.

Prediction of Brain States of Concentration and Relaxation in Real Time with Portable Electroencephalographs