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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| Published in: | IEEE transactions on human-machine systems Vol. 47; no. 4; pp. 550 - 563 |
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| Format: | Journal Article |
| Language: | English |
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IEEE
01.08.2017
The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
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| ISSN: | 2168-2291, 2168-2305 |
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| Abstract | 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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| AbstractList | 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 [Formula Omitted], making them more suitable for real-time applications. 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. |
| Author | Wu, Dongrui |
| Author_xml | – sequence: 1 givenname: Dongrui surname: Wu fullname: Wu, Dongrui email: drwu09@gmail.com organization: DataNova, Clifton Park, NY, USA |
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| SubjectTerms | 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 |
| Title | Online and Offline Domain Adaptation for Reducing BCI Calibration Effort |
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