Toward unsupervised adaptation of LDA for brain-computer interfaces

C. Vidaurre; M. Kawanabe; P. Von Bünau; B. Blankertz; K. R. Müller

doi:10.1109/TBME.2010.2093133

Toward unsupervised adaptation of LDA for brain-computer interfaces

C. Vidaurre, M. Kawanabe, P. Von Bünau, B. Blankertz, K. R. Müller

Research output: Contribution to journal › Article › peer-review

250 Citations (Scopus)

Abstract

There is a step of significant difficulty experienced by brain-computer interface (BCI) users when going from the calibration recording to the feedback application. This effect has been previously studied and a supervised adaptation solution has been proposed. In this paper, we suggest a simple unsupervised adaptation method of the linear discriminant analysis (LDA) classifier that effectively solves this problem by counteracting the harmful effect of nonclass-related nonstationarities in electroencephalography (EEG) during BCI sessions performed with motor imagery tasks. For this, we first introduce three types of adaptation procedures and investigate them in an offline study with 19 datasets. Then, we select one of the proposed methods and analyze it further. The chosen classifier is offline tested in data from 80 healthy users and four high spinal cord injury patients. Finally, for the first time in BCI literature, we apply this unsupervised classifier in online experiments. Additionally, we show that its performance is significantly better than the state-of-the-art supervised approach.

Original language	English
Pages (from-to)	587-597
Number of pages	11
Journal	IEEE Transactions on Biomedical Engineering
Volume	58
Issue number	3 PART 1
DOIs	https://doi.org/10.1109/TBME.2010.2093133
Publication status	Published - 2011 Mar

Bibliographical note

Funding Information:
Manuscript received August 27, 2010; revised October 10, 2010; accepted October 10, 2010. Date of publication November 18, 2010; date of current version February 18, 2011. This work was supported by the European Union (EU) MC-IEF-040666, IST-PASCAL2 Network of Excellence, ICT-216886 and by the Bundesministerium für Bildung und Forschung (BMBF) FKZ-01IBE01A/B. Asterisk indicates corresponding author. *C. Vidaurre is with the Department of Machine Learning, Berlin Institute of Technology, 10623 Berlin, Germany.

Keywords

Adaptive signal processing
brain computer interfaces
linear discriminant analysis
unsupervised learning

ASJC Scopus subject areas

Biomedical Engineering

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10.1109/TBME.2010.2093133

Cite this

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abstract = "There is a step of significant difficulty experienced by brain-computer interface (BCI) users when going from the calibration recording to the feedback application. This effect has been previously studied and a supervised adaptation solution has been proposed. In this paper, we suggest a simple unsupervised adaptation method of the linear discriminant analysis (LDA) classifier that effectively solves this problem by counteracting the harmful effect of nonclass-related nonstationarities in electroencephalography (EEG) during BCI sessions performed with motor imagery tasks. For this, we first introduce three types of adaptation procedures and investigate them in an offline study with 19 datasets. Then, we select one of the proposed methods and analyze it further. The chosen classifier is offline tested in data from 80 healthy users and four high spinal cord injury patients. Finally, for the first time in BCI literature, we apply this unsupervised classifier in online experiments. Additionally, we show that its performance is significantly better than the state-of-the-art supervised approach.",

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Toward unsupervised adaptation of LDA for brain-computer interfaces

Abstract

Bibliographical note

Keywords

ASJC Scopus subject areas

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