Computer Vision News 16 WACV Poster Presentation data. The non-deterministic nature of brain signals poses difficulties in extracting useful information, making it a complex and intricate process. “If I’m looking at a picture of a dog and record the EEG brain signal, when I repeat the experiment, the signal I get is going to be different because thoughts contain so many biases,” he explains. “While seeing one thing, we might think about something else or hear something from our surroundings, which makes it very difficult to extract useful information from the EEG data.” Synthesizing the information once extracted from the EEG was a further challenge. To solve this, he used a self-supervised strategy to train every approach, avoiding relying on supervised settings or ground truth. EEG has been compared to a fingerprint for each and every person. Looking ahead, Prajwal hopes that future research explores the possibility of generalized EEG feature extraction methods. Currently, datasets are very controlled, but is it possible to move towards generalization and a better strategy for synthesizing images? Recently, he has demonstrated the potential for deploying the model in a live setting by conducting an image-to-image translation experiment.
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