Online and Offline Domain Adaptation for Reducing BCI Calibration Effort

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 who...

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Bibliographic Details
Published in:IEEE transactions on human-machine systems Vol. 47; no. 4; pp. 550 - 563
Main Author: Wu, Dongrui
Format: Journal Article
Language:English
Published: New York IEEE 01.08.2017
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Adaptation
Algorithms
Brain
Brain models
Brain–computer interface (BCI)
Calibration
Classifiers
Cost engineering
Data models
domain adaptation (DA)
Earphones
EEG
Electroencephalography
event-related potential (ERP)
Headphones
Indexing
Real time
Regularization
transfer learning
Visualization
ISSN:2168-2291, 2168-2305
Online Access:Get full text
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Summary: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 <inline-formula> <tex-math notation="LaTeX">\text{50}\%</tex-math></inline-formula>, making them more suitable for real-time applications.
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ISSN:2168-2291
2168-2305
DOI:10.1109/THMS.2016.2608931