Optimising image feature zero-shot-learning with EEG

Once our thoughts can be directly understood and actioned, huge gains in terms of productivity and usability from consumer to professional applications can be envisioned. However, in its current state, BCI systems are limited in use to research and medical applications (in assistive technologies and in diagnoses).<br><br>This project presents a BCI system for image retrieval where the system is able to identify the specific image that the user is looking at.

In contrast to the majority of BCI research, we contribute work under several real world restrictions. Namely, our processing must be fully automated, use a recording technology available to consumers, and should be able to search for images not present during training, i.e. we apply zero-shot learning.<br><br>To achieve this goal, we first investigate a discriminative feature extraction process for EEG brain data and establish a baseline evaluation of exemplar and category-level decoding.