Abstract
Brain decoding—the process of inferring a person’s momentary cognitive state from their brain activity—has enormous potential in the field of human-computer interaction. In this study we propose a zero-shot EEG-to-image brain decoding approach which makes use of state-of-the-art EEG preprocessing and feature selection methods, and which maps EEG activity to biologically inspired computer vision and linguistic models. We apply this approach to solve the problem of identifying viewed images from recorded brain activity in a reliable and scalable way. We demonstrate competitive decoding accuracies across two EEG datasets, using a zero-shot learning framework more applicable to real-world image retrieval than traditional classification techniques.
Highlights
Research in the field of Brain-Computer Interfaces (BCI) began in the 1970s [1] with the aim of providing a new, intuitive, and rich method of communication between computer systems and their users
All the exemplar decoding results we present are significantly above chance (50%), indicating a mapping between EEG activity and the image feature sets we have chosen that can be used for zero-shot brain decoding
In this paper we proposed an approach to zero-shot image retrieval in EEG data using a novel combination of feature sets, feature selection, and regression modeling
Summary
Research in the field of Brain-Computer Interfaces (BCI) began in the 1970s [1] with the aim of providing a new, intuitive, and rich method of communication between computer systems and their users. These methods involve measuring some aspect of neural activity and inferring or decoding an intended action or particular characteristic of the user’s cognitive state. Other relevant applications include identifying the image that a user is viewing, usually referred to as image retrieval, which is of particular interest in the fields of visual attention applied to advertising and marketing, in searching and organising large collections of images, and in reducing distractions during driving, to name a few.
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