Self-Calibrating BCIs: Ranking and Recovery of Mental Targets Without Labels
This addresses the challenge of brain-computer interfaces for mental target recovery without labels, offering a novel self-calibration approach that is not incremental.
The paper tackles the problem of recovering a mental target from EEG and image data without labeled information, presenting the CURSOR algorithm that predicts image similarity scores correlating with human judgments, ranks stimuli, and generates new stimuli validated in a user study with 53 participants.
We consider the problem of recovering a mental target (e.g., an image of a face) that a participant has in mind from paired EEG (i.e., brain responses) and image (i.e., perceived faces) data collected during interactive sessions without access to labeled information. The problem has been previously explored with labeled data but not via self-calibration, where labeled data is unavailable. Here, we present the first framework and an algorithm, CURSOR, that learns to recover unknown mental targets without access to labeled data or pre-trained decoders. Our experiments on naturalistic images of faces demonstrate that CURSOR can (1) predict image similarity scores that correlate with human perceptual judgments without any label information, (2) use these scores to rank stimuli against an unknown mental target, and (3) generate new stimuli indistinguishable from the unknown mental target (validated via a user study, N=53).