7 DAILY WACV Saturday Learning Robust Deep Visual … capture. This data can then be stored in a computer, but how do you extract the visual information from the EEG signal? “We use a contrastive learning approach to extract the features from the EEG data,” Prajwal explains. “Specifically, a triplet loss formulation. Once we have these features, a StyleGAN handles the synthesis part. Different generative networks have been used in the past, but for our work, we used StyleGAN, which generates photorealistic images and has been quite on trend recently.” To address the scarcity of large datasets required to train deep learning architectures, researchers used a StyleGAN-ADA network that can generalize across different datasets. Previous methods all dealt with generating images from particular EEG datasets instead of working more broadly. The potential applications of this work are significant, particularly in improving the quality of life of people with certain medical conditions, such as individuals who are mute or paralyzed, yet their brains remain active. In those scenarios, an EEG cap could be placed over that person’s scalp to record their brain signals and, ultimately, reconstruct their thoughts. Prajwal tells us the most challenging aspect of this research has been handling the noisy nature of EEG Each image is generated with different EEG signals across differentclasses, Thoughtviz dataset.
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