# Self-Calibrating BCIs: Ranking and Recovery of Mental Targets Without Labels

arXiv: https://arxiv.org/abs/2506.11151 · HTML: https://mlsanity.com/paper/2506.11151/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 21 of its 84 references that are in the map.
- First posted on arXiv 11 Jun 2025.

## Builds on (most cited first, 21 of 21)

- [Progressive Growing of GANs for Improved Quality, Stability, and Variation](https://mlsanity.com/paper/1710.10196/) (2017)
- [Optuna: A Next-generation Hyperparameter Optimization Framework](https://mlsanity.com/paper/1907.10902/) (2019)
- [Datasheets for Datasets](https://mlsanity.com/paper/1803.09010/) (2018)
- [Model Cards for Model Reporting](https://mlsanity.com/paper/1810.03993/) (2018)
- [EEGNet: A Compact Convolutional Network for EEG-based Brain-Computer Interfaces](https://mlsanity.com/paper/1611.08024/) (2016)
- [GANSpace: Discovering Interpretable GAN Controls](https://mlsanity.com/paper/2004.02546/) (2020)
- [Closed-Form Factorization of Latent Semantics in GANs](https://mlsanity.com/paper/2007.06600/) (2020)
- [Scalable Global Optimization via Local Bayesian Optimization](https://mlsanity.com/paper/1910.01739/) (2019)
- [Re-Examining Linear Embeddings for High-Dimensional Bayesian Optimization](https://mlsanity.com/paper/2001.11659/) (2020)
- [DreamDiffusion: Generating High-Quality Images from Brain EEG Signals](https://mlsanity.com/paper/2306.16934/) (2023)
- [Object classification from randomized EEG trials](https://mlsanity.com/paper/2004.06046/) (2020)
- [Evaluating representations by the complexity of learning low-loss predictors](https://mlsanity.com/paper/2009.07368/) (2020)
- [Learning from Label Proportions in Brain-Computer Interfaces: Online Unsupervised Learning with Guarantees](https://mlsanity.com/paper/1701.07213/) (2017)
- [First Contact: Unsupervised Human-Machine Co-Adaptation via Mutual Information Maximization](https://mlsanity.com/paper/2205.12381/) (2022)
- [Interaction-Grounded Learning with Action-inclusive Feedback](https://mlsanity.com/paper/2206.08364/) (2022)
- [Empirical Evaluation and Theoretical Analysis for Representation Learning: A Survey](https://mlsanity.com/paper/2204.08226/) (2022)
- [UMM: Unsupervised Mean-difference Maximization](https://mlsanity.com/paper/2306.11830/) (2023)
- [Learning to Guide and to Be Guided in the Architect-Builder Problem](https://mlsanity.com/paper/2112.07342/) (2021)
- [IFTT-PIN: A Self-Calibrating PIN-Entry Method](https://mlsanity.com/paper/2407.02269/) (2024)
- [Interactive introduction to self-calibrating interfaces](https://mlsanity.com/paper/2212.05766/) (2022)
- [Physiological Data: Challenges for Privacy and Ethics](https://mlsanity.com/paper/2405.15272/) (2024)

## Built on by (most cited first, 1 of 1)

- [MindPilot: Closed-loop Visual Stimulation Optimization for Brain Modulation with EEG-guided Diffusion](https://mlsanity.com/paper/2602.10552/) (2026)

## Abstract (arXiv)

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).