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Combination of the Motor Imagery EEG (MI-EEG) imaging and Deep Convolutional Neural Network is a prospective recognition method in brain computer interface. Nowadays, the frequency or timefrequency analysis has been applied to each channel of MI-EEG signal to obtain a spatio-frequency or timefrequency image, and even the images from several channels are infused to generate a combined image. However, the real position information of channels or electrodes is lost in these MI-EEG images, and this is contradictory to the activation area of MI-tasks. In this paper, the MI period and the frequency band covered by &#x03BC; and &#x03B2; rhythms are divided into ten time windows and three sub-bands, respectively. Then, for each electrode, Fast Fourier Transform (FFT) is employed to transform each time window to spectrum, and its inverse FFT is calculated for each sub-band. The time-domain powers of ten time windows are averaged for the same sub-band. So, three average powers are generated as the time-frequency features of each electrode of MI-EEG. They are further arranged to the electrode coordinate figure by using Clough-Tocher interpolation algorithm, and a complicated image, in which the time-frequency features are correctly located at the real position of each electrode, is obtained to embody the MI-EEG in detail. Furthermore, a VGG network is modified to perform effective recognition for MI-EEG image, and it is called mVGG. Extensive experiments are conducted on three publicly available datasets, and the 10-folds cross validation accuracies of 88.62%, 92.28% and 96.86% are achieved respectively, and they are higher than that of the state-of-the-art imaging methods. Kappa values and ROC curves demonstrate our method has lower class skew and error costs. The experimental results show that the effectiveness of proposed MI-EEG imaging method, and it is well-matched with mVGG.

A Novel MI-EEG Imaging With the Location Information of Electrodes