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

• Published in: IEEE access 2020, Vol.8, p.3197-3211
• Main Authors: Li, Ming-Ai, Han, Jian-Fu, Duan, Li-Juan
• Format: Article
• Language: English
• Subjects: Algorithms; Artificial neural networks; Brain computer interface; Channels; Computed tomography; convolutional neural network; Electrodes; Electroencephalography; Fast Fourier transformations; Fourier transforms; Frequencies; Frequency analysis; Human-computer interface; Imagery; Imaging; Interpolation; interpolation method; machine learning; MI-EEG imaging method; Microsoft Windows; Object recognition; Time-frequency analysis; Windows (intervals)
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2019.2962740

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 μ and β 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.