A BCI using MEGvision and multilayer neural network - channel optimization and main lobe contribution analysis -

Multilayer neural networks (MLNN) and the FFT amplitude of brain waves have been applied to dasiaBrain Computer Interfacepsila (BCI). In this paper, a magnetoencephalograph (MEG) system, dasiaMEGvisionpsila developed by Yokogawa Corporation, is used to measure brain activities. MEGvision is a 160-channel whole-head MEG system. Channels are selected from 8 main regions, a frontal lobe, a temporal lobe, a parietal lobe and a occipital lobe, located in the left and the right sides of the brain. The 8 channels, located at the central point in the 8 lobes, are initially selected. Optimum channels are searched for in the same lobe as the initial channels in order to achieve high classification accuracy. Two subjects and four mental tasks, including relaxed situation, multiplication, playing sport and rotating an object, are used. The brain waves are measured 10 times for one subject and one mental task. Among them, 8 data sets are used for training the MLNN, and the remaining 2 data sets are used for testing. 5 kinds of combinations of 2 data sets are selected for testing. Rates of correct classification by using the initial channels are 82:5 ~ 90%. By optimizing the channels, the accuracy is improved up to 85:0 ~97:5%, which is very high accuracy. Furthermore, contributions of the brain waves in the 8 lobes are analyzed.