Abstract: This research is motivated to gift the ability of expression to the speech deprived by building a module that extracts thoughts from the human brain and presents it to the external world as a digital image. This novel approach reconstructs the same images of different colours from the EEG brain waves. Standalone datasets are used to train the model, obtained using the Enobio8 headset. EEG signals are acquired by providing six different shapes of three unique colors as the visual stimuli. After processing these signals, EEG features are extracted using a convolutional Neural Network (CNN). The extracted features alongside noise are passed as an input to conditional Deep Convolutional Generative Adversarial Networks (cDCGANs) and conditional Generative Adversarial Networks (cGANs), which reconstruct the visual stimuli corresponding to the EEG data. The results obtained by the same are recorded and examined. The paper explicitly compares the working and performance of the cGAN and cDCGAN models.

Keywords: EEG, Image reconstruction, Deep Learning, Brain Media, Generative adversarial networks (GANs).