A meta-learning BCI for estimating decision confidence

• Published in: Journal of neural engineering Vol. 19; no. 4; pp. 46009 - 46025
• Main Authors: Tremmel, Christoph, Fernandez-Vargas, Jacobo, Stamos, Dimitris, Cinel, Caterina, Pontil, Massimiliano, Citi, Luca, Poli, Riccardo
• Format: Journal Article
• Language: English
• Published: IOP Publishing 01.08.2022
• Subjects: brain–computer interfaces; decision confidence prediction; decision making; EEG; meta learning
• ISSN: 1741-2560; 1741-2552; 1741-2552
• DOI: 10.1088/1741-2552/ac7ba8

Objective. We investigated whether a recently introduced transfer-learning technique based on meta-learning could improve the performance of brain–computer interfaces (BCIs) for decision-confidence prediction with respect to more traditional machine learning methods. Approach. We adapted the meta-learning by biased regularisation algorithm to the problem of predicting decision confidence from electroencephalography (EEG) and electro-oculogram (EOG) data on a decision-by-decision basis in a difficult target discrimination task based on video feeds. The method exploits previous participants’ data to produce a prediction algorithm that is then quickly tuned to new participants. We compared it with with the traditional single-subject training almost universally adopted in BCIs, a state-of-the-art transfer learning technique called domain adversarial neural networks, a transfer-learning adaptation of a zero-training method we used recently for a similar task, and with a simple baseline algorithm. Main results. The meta-learning approach was significantly better than other approaches in most conditions, and much better in situations where limited data from a new participant are available for training/tuning. Meta-learning by biased regularisation allowed our BCI to seamlessly integrate information from past participants with data from a specific user to produce high-performance predictors. Its robustness in the presence of small training sets is a real-plus in BCI applications, as new users need to train the BCI for a much shorter period. Significance. Due to the variability and noise of EEG/EOG data, BCIs need to be normally trained with data from a specific participant. This work shows that even better performance can be obtained using our version of meta-learning by biased regularisation.