Tremmel, Christoph;
Fernandez-Vargas, Jacobo;
Stamos, Dimitris;
Cinel, Caterina;
Pontil, Massimiliano;
Citi, Luca;
Poli, Riccardo;
(2022)
A meta-learning BCI for estimating decision confidence.
Journal of Neural Engineering
, 19
(4)
, Article 046009. 10.1088/1741-2552/ac7ba8.
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Abstract
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.
| Type: | Article |
|---|---|
| Title: | A meta-learning BCI for estimating decision confidence |
| Location: | England |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1088/1741-2552/ac7ba8 |
| Publisher version: | https://doi.org/10.1088/1741-2552/ac7ba8 |
| Language: | English |
| Additional information: | Original Content from this work may be used under the terms of the Creative Commons Attribution 4.0 licence. Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI. |
| Keywords: | Science & Technology, Technology, Life Sciences & Biomedicine, Engineering, Biomedical, Neurosciences, Engineering, Neurosciences & Neurology, brain-computer interfaces, EEG, meta learning, decision confidence prediction, decision making, BRAIN-COMPUTER INTERFACES, COMMUNICATION, ACCUMULATION, COMPUTATION, VIGILANCE, ARTIFACT, MEMORY |
| UCL classification: | UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science UCL > Provost and Vice Provost Offices > UCL BEAMS UCL |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10153384 |
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