Online and Offline Domain Adaptation for Reducing BCI Calibration Effort

By Dongrui Wu. 2017

105 citations recorded by the source corpus.

Many real-world brain–computer interface (BCI) applications rely on single-trial classification of event-related potentials (ERPs) in EEG signals. However, because different subjects have different neural responses to even the same stimulus, it is very difficult to build a generic ERP classifier whose parameters fit all subjects. The classifier needs to be calibrated for each individual subject, using some labeled subject-specific data. This paper proposes both online and offline weighted adaptation regularization (wAR) algorithms to reduce this calibration effort, i.e., to minimize the amount of labeled subject-specific EEG data required in BCI calibration, and hence to increase the utility of the BCI system. We demonstrate using a visually evoked potential oddball task and three different EEG headsets that both online and offline wAR algorithms significantly outperform several other algorithms. Moreover, through source domain selection, we can reduce their computational cost by about $\text{50}\%$, making them more suitable for real-time applications.

Subfields: Cognitive Neuroscience, Human-Computer Interaction

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Citations in the corpus

Unsupervised Model Adaptation for Continual Semantic Segmentation (2020, methodology): “This model adaptation setting been explored for non-deep models (Dredze and Crammer 2008; Jain and Learned-Miller 2011; Wu 2016), but these works cannot be extended to semantic segmentation tasks.”
Transfer Learning for EEG-Based Brain–Computer Interfaces: A Review of Progress Made Since 2016 (2020, unknown): “Wu [74] proposed weighted adaptation regularization (wAR) for cross-subject transfers in ERP-based BCIs in both online and ofﬂine settings.”
A Survey on Negative Transfer (2020, background): “This phenomenon has been found in many parameter sensitivity experiments [7], [10].”
Transfer Learning for Brain–Computer Interfaces: A Euclidean Space Data Alignment Approach (2018, background): “Transfer learning (TL) [24], which utilizes information in source domains to improve the learning performance in a target domain, has also been successfully used for BCIs [13], [36], [37], [39], [40].”
EEG-Based Brain-Computer Interfaces (BCIs): A Survey of Recent Studies on Signal Sensing Technologies and Computational Intelligence Approaches and Their Applications (2020, methodology): “For subject-to-subject transfer in single-trial ERP classification, Wu [70] proposed both online and offline weighted adaptation regularisation (wAR) algorithms to reduce the calibration effort.”
Sequential Unsupervised Domain Adaptation through Prototypical Distributions (2020, methodology): “This learning setting for domain adaptation has been explored for non-deep models [23–25].”