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 | |
| Publication status | Published - 2011 Mar 1 |
| Externally published | Yes |
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Keywords
- Adaptive signal processing
- brain computer interfaces
- linear discriminant analysis
- unsupervised learning
ASJC Scopus subject areas
- Biomedical Engineering
Cite this
Toward unsupervised adaptation of LDA for brain-computer interfaces. / Vidaurre, C.; Kawanabe, M.; Von Bünau, P.; Blankertz, B.; Muller, Klaus.
In: IEEE Transactions on Biomedical Engineering, Vol. 58, No. 3 PART 1, 01.03.2011, p. 587-597.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Toward unsupervised adaptation of LDA for brain-computer interfaces
AU - Vidaurre, C.
AU - Kawanabe, M.
AU - Von Bünau, P.
AU - Blankertz, B.
AU - Muller, Klaus
PY - 2011/3/1
Y1 - 2011/3/1
N2 - 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.
AB - 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.
KW - Adaptive signal processing
KW - brain computer interfaces
KW - linear discriminant analysis
KW - unsupervised learning
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U2 - 10.1109/TBME.2010.2093133
DO - 10.1109/TBME.2010.2093133
M3 - Article
C2 - 21095857
AN - SCOPUS:79952167211
VL - 58
SP - 587
EP - 597
JO - IEEE Transactions on Biomedical Engineering
JF - IEEE Transactions on Biomedical Engineering
SN - 0018-9294
IS - 3 PART 1
ER -