MindCeive: Perceiving human imagination using CNN-GRU and GANs

Abstract

Neuroscience has made astonishing advancements in understanding the human brain with the help of Brain-Computer Interface. Recent contributions in the field of Artificial Intelligence by different researchers made it possible to perceive human imagination by decoding brain signals. Generating visual stimuli perceived by humans will help in analyzing how the human brain works and behaves to different perceptual experiences. Different techniques like Electroencephalography, Magnetoencephalography, functional Magnetic Resonance Imaging, etc. are used to capture brain signals. Electroencephalography signals are non-invasive, low cost, and also have high temporal resolution, therefore they are preferred. Machine learning models are used to extract important features from these signals. These extracted features are then used by Generative Adversarial Network to generate images representing human imagination. This work uses Electroencephalography signals to generate realistic images. The task of extracting important features from Electroencephalography signals is achieved using Convolutional Neural Network and Gated Recurrent Unit based feature extractor. The proposed feature extractor accomplishes better classification accuracy than existing models. By using these extracted features in combination with proposed novel architecture of Generative Adversarial Network, realistic images of objects imagined by humans are generated. The proposed MindCeive approach outperforms previous works by showing improvement in various performance metrics such as Classification Accuracy, Inception Score, and Class Diversity Score.