A conditional input-based GAN for generating spatio-temporal motor imagery electroencephalograph data.

Brain Computer Interface is an emerging technology for assisting patients having long term disability. Electroencephalography is the best technique for recording neural activities, however, recording bio signal data from patients is a hectic task. Therefore, Data augmentation using generative adversarial networks is expected to solve this problem. In this paper, we present temporal generative adversarial network with conditional input labels to learn the spatio-temporal features of electroencephalograph and generate realistic data. In order to evaluate the performance of proposed model, we introduce qualitative, quantitative and feature visualization techniques on motor imagery electroencephalograph dataset. Further, we used 1-dimensional temporal convolutional neural network to investigate whether augmented data can replace real data or not for training models. The presented data augmentation framework was compared to three more data augmentation techniques like: copula generative adversarial network, conditional tabular generative adversarial network and tabular variational autoencoder. Our presented technique showed significantly better results to other data augmentation techniques for Electroencephalograph data. The experimental results show that the value of Kullback–Leibler Divergence (KL Divergence) and Inverted Kolmogorov Smirnov D static test (KS test) of data generated by proposed model is the lowest i.e., 0.8940 and 0.7674 respectively. Further, we have tested exhaustively our new data and used a new testing strategy achieving an accuracy of 94.1%. Finally, it is concluded that a time series-based data augmentation technique for Electroencephalograph can reduce long calibration process and thus improve the overall performance of motor imagery user action classification. [ABSTRACT FROM AUTHOR]