Active semi-supervised transfer learning (ASTL) for offline BCI calibration
Open accessAbstract
Single-trial classification of event-related potentials in electroencephalogram (EEG) signals is a very important paradigm of brain-computer interface (BCI). Because of individual differences, usually some subject-specific calibration data are required to tailor the classifier for each subject. Transfer learning has been extensively used to reduce such calibration data requirement, by making use of auxiliary data from similar/relevant subjects/tasks. However, all previous research assumes that all auxiliary data have been labeled. This paper considers a more general scenario, in which part of the auxiliary data could be unlabeled. We propose active semi-supervised transfer learning (ASTL) for offline BCI calibration, which integrates active learning, semi-supervised learning, and transfer learning. Using a visual evoked potential oddball task and three different EEG headsets, we demonstrate that ASTL can achieve consistently good performance across subjects and headsets, and it outperforms some state-of-the-art approaches in the literature.
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Transfer learning has been successfully used for BCIs.
“Transfer learning (TL) [23], which utilizes information in source domains to improve the learning performance in a target domain, has also been successfully used for BCIs [12], [35], [36], [38], [39].”
From Transfer Learning for Brain–Computer Interfaces: A Euclidean Space Data Alignment Approach (2020) · cites this paper as [35]
There is no closed-form solution to the Riemannian mean, and it is usually computed by an iterative gradient descent algorithm.
“There is no closed-form solution to (3), and it is usually computed by an iterative gradient descent algorithm [8].”
From Transfer Learning for Brain–Computer Interfaces: A Euclidean Space Data Alignment Approach (2020) · cites this paper as [8]
The Weighted Adaptation Regularization section defines equation (3) as class-specific sample weights, not a Riemannian mean. Searches for a Riemannian mean, a closed-form solution, and gradient descent found no passage in our copy addressing the claim. (LLM, conf=0.76)
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- Authors
- Wu, D.
- Corresponding author
- Wu, Dongrui
- ORCID
- Wu, Dongrui
- Venue
- 2017 IEEE International Conference on Systems, Man, and Cybernetics (SMC)
- Publisher
- IEEE
- Published
- 2017-10
- Pages
- 246-251
- ISSN
- 1062-922X (ISSN-L)
- OpenAlex
- W2964288883
- Cited by
- 16
- References
- 26
- Citations by year
- 2026: 1 · 2025: 1 · 2022: 6 · 2021: 1 · 2020: 3 · 2019: 3 · 2018: 1
- FWCI
- 3.13
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- Conference proceedings/Conference proceedings - IEEE International Conference on Systems, Man, and Cybernetics (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › EEG and Brain-Computer Interfaces
- Physical Sciences › Computer Science › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
- Physical Sciences › Computer Science › Human-Computer Interaction › Gaze Tracking and Assistive Technology
- Keywords
- offline calibration, single-trial classification, event-related potentials, EEG, active learning (machine learning), semi-supervised learning, transfer learning, subject-specific calibration, EEG headset
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