A meta-learning BCI for estimating decision confidence

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 EEG and 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 (DANN), 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.