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An algorithm for idle-state detection in motor-imagery-based brain-computer interface.

Zhang D, Wang Y, Gao X, Hong B, Gao S - Comput Intell Neurosci (2007)

Bottom Line: Moreover, because of the diversity of idle state, detecting idle state without training samples is as important as classifying MI tasks.Then Fisher discriminant analysis (FDA) was employed in the design of two two-class classifiers for completion of detecting each task, respectively.A final result with mean square error of 0.30 was obtained on the testing set.

View Article: PubMed Central - PubMed

Affiliation: Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing 100084, China.

ABSTRACT
For a robust brain-computer interface (BCI) system based on motor imagery (MI), it should be able to tell when the subject is not concentrating on MI tasks (the "idle state") so that real MI tasks could be extracted accurately. Moreover, because of the diversity of idle state, detecting idle state without training samples is as important as classifying MI tasks. In this paper, we propose an algorithm for solving this problem. A three-class classifier was constructed by combining two two-class classifiers, one specified for idle-state detection and the other for these two MI tasks. Common spatial subspace decomposition (CSSD) was used to extract the features of event-related desynchronization (ERD) in two motor imagery tasks. Then Fisher discriminant analysis (FDA) was employed in the design of two two-class classifiers for completion of detecting each task, respectively. The algorithm successfully provided a way to solve the problem of "idle-state detection without training samples." The algorithm was applied to the dataset IVc from BCI competition III. A final result with mean square error of 0.30 was obtained on the testing set. This is the winning algorithm in BCI competition III. In addition, the algorithm was also validated by applying to the EEG data of an MI experiment including "idle" task.

No MeSH data available.


Distribution of classification results in Step 1.
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Figure 8: Distribution of classification results in Step 1.

Mentions: The basic assumption was that during relax task there isno obvious ERD over somatosensory or motor cortex. This assumptionis shown to be reasonable according to the final results.Figure 7 displays the averaged spatial mapping ofrelax (calculated in a same way as inFigure 1) in the testing set. There is no obvious ERDin region A1 and A2. Figure 8 shows the classificationresults of the samples in the testing set by these two classifiersand the true labels are given by different legends. Most samplesof relax are located in the second quadrant, whileright-foot and left-hand samples are in thefirst and third quadrants. This distribution is in accordance withthe analysis inTable 1.


An algorithm for idle-state detection in motor-imagery-based brain-computer interface.

Zhang D, Wang Y, Gao X, Hong B, Gao S - Comput Intell Neurosci (2007)

Distribution of classification results in Step 1.
© Copyright Policy - open-access
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC1994518&req=5

Figure 8: Distribution of classification results in Step 1.
Mentions: The basic assumption was that during relax task there isno obvious ERD over somatosensory or motor cortex. This assumptionis shown to be reasonable according to the final results.Figure 7 displays the averaged spatial mapping ofrelax (calculated in a same way as inFigure 1) in the testing set. There is no obvious ERDin region A1 and A2. Figure 8 shows the classificationresults of the samples in the testing set by these two classifiersand the true labels are given by different legends. Most samplesof relax are located in the second quadrant, whileright-foot and left-hand samples are in thefirst and third quadrants. This distribution is in accordance withthe analysis inTable 1.

Bottom Line: Moreover, because of the diversity of idle state, detecting idle state without training samples is as important as classifying MI tasks.Then Fisher discriminant analysis (FDA) was employed in the design of two two-class classifiers for completion of detecting each task, respectively.A final result with mean square error of 0.30 was obtained on the testing set.

View Article: PubMed Central - PubMed

Affiliation: Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing 100084, China.

ABSTRACT
For a robust brain-computer interface (BCI) system based on motor imagery (MI), it should be able to tell when the subject is not concentrating on MI tasks (the "idle state") so that real MI tasks could be extracted accurately. Moreover, because of the diversity of idle state, detecting idle state without training samples is as important as classifying MI tasks. In this paper, we propose an algorithm for solving this problem. A three-class classifier was constructed by combining two two-class classifiers, one specified for idle-state detection and the other for these two MI tasks. Common spatial subspace decomposition (CSSD) was used to extract the features of event-related desynchronization (ERD) in two motor imagery tasks. Then Fisher discriminant analysis (FDA) was employed in the design of two two-class classifiers for completion of detecting each task, respectively. The algorithm successfully provided a way to solve the problem of "idle-state detection without training samples." The algorithm was applied to the dataset IVc from BCI competition III. A final result with mean square error of 0.30 was obtained on the testing set. This is the winning algorithm in BCI competition III. In addition, the algorithm was also validated by applying to the EEG data of an MI experiment including "idle" task.

No MeSH data available.