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Discriminative Common Spatial Pattern Sub-bands Weighting Based on Distinction Sensitive Learning Vector Quantization Method in Motor Imagery Based Brain-computer Interface.

Jamaloo F, Mikaeili M - J Med Signals Sens (2015 Jul-Sep)

Bottom Line: Common spatial pattern (CSP) is a method commonly used to enhance the effects of event-related desynchronization and event-related synchronization present in multichannel electroencephalogram-based brain-computer interface (BCI) systems.Finally, after the classification of the weighted features using a support vector machine classifier, the performance of the suggested method has been compared with the existing methods based on frequency band selection, on the same BCI competitions datasets.The results show that the proposed method yields superior results on "ay" subject dataset compared against existing approaches such as sub-band CSP, filter bank CSP (FBCSP), discriminative FBCSP, and sliding window discriminative CSP.

View Article: PubMed Central - PubMed

Affiliation: Department of Engineering, Shahed University, Tehran, Iran.

ABSTRACT
Common spatial pattern (CSP) is a method commonly used to enhance the effects of event-related desynchronization and event-related synchronization present in multichannel electroencephalogram-based brain-computer interface (BCI) systems. In the present study, a novel CSP sub-band feature selection has been proposed based on the discriminative information of the features. Besides, a distinction sensitive learning vector quantization based weighting of the selected features has been considered. Finally, after the classification of the weighted features using a support vector machine classifier, the performance of the suggested method has been compared with the existing methods based on frequency band selection, on the same BCI competitions datasets. The results show that the proposed method yields superior results on "ay" subject dataset compared against existing approaches such as sub-band CSP, filter bank CSP (FBCSP), discriminative FBCSP, and sliding window discriminative CSP.

No MeSH data available.


Variances of the training electroencephalogram signals for subject “aa” after projection onto the most discriminative pairs of directions obtained by basic common spatial pattern. In this figure, blue and red points are training examples and black circles are support vectors of the classifier and green and magnet points are test examples for each class
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Figure 1: Variances of the training electroencephalogram signals for subject “aa” after projection onto the most discriminative pairs of directions obtained by basic common spatial pattern. In this figure, blue and red points are training examples and black circles are support vectors of the classifier and green and magnet points are test examples for each class

Mentions: For further clarification of the properties associated with our proposed method, Figures 1 and 2 show the variances of the training EEG for subject “aa” after projection onto the most important discriminative pairs of directions obtained from the basic CSP and from our method, respectively. It means that for each point in these figures, the horizontal coordinate is the variance of the first row of the spatially filtered trial, and the vertical one is the variance of the last row.


Discriminative Common Spatial Pattern Sub-bands Weighting Based on Distinction Sensitive Learning Vector Quantization Method in Motor Imagery Based Brain-computer Interface.

Jamaloo F, Mikaeili M - J Med Signals Sens (2015 Jul-Sep)

Variances of the training electroencephalogram signals for subject “aa” after projection onto the most discriminative pairs of directions obtained by basic common spatial pattern. In this figure, blue and red points are training examples and black circles are support vectors of the classifier and green and magnet points are test examples for each class
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4528353&req=5

Figure 1: Variances of the training electroencephalogram signals for subject “aa” after projection onto the most discriminative pairs of directions obtained by basic common spatial pattern. In this figure, blue and red points are training examples and black circles are support vectors of the classifier and green and magnet points are test examples for each class
Mentions: For further clarification of the properties associated with our proposed method, Figures 1 and 2 show the variances of the training EEG for subject “aa” after projection onto the most important discriminative pairs of directions obtained from the basic CSP and from our method, respectively. It means that for each point in these figures, the horizontal coordinate is the variance of the first row of the spatially filtered trial, and the vertical one is the variance of the last row.

Bottom Line: Common spatial pattern (CSP) is a method commonly used to enhance the effects of event-related desynchronization and event-related synchronization present in multichannel electroencephalogram-based brain-computer interface (BCI) systems.Finally, after the classification of the weighted features using a support vector machine classifier, the performance of the suggested method has been compared with the existing methods based on frequency band selection, on the same BCI competitions datasets.The results show that the proposed method yields superior results on "ay" subject dataset compared against existing approaches such as sub-band CSP, filter bank CSP (FBCSP), discriminative FBCSP, and sliding window discriminative CSP.

View Article: PubMed Central - PubMed

Affiliation: Department of Engineering, Shahed University, Tehran, Iran.

ABSTRACT
Common spatial pattern (CSP) is a method commonly used to enhance the effects of event-related desynchronization and event-related synchronization present in multichannel electroencephalogram-based brain-computer interface (BCI) systems. In the present study, a novel CSP sub-band feature selection has been proposed based on the discriminative information of the features. Besides, a distinction sensitive learning vector quantization based weighting of the selected features has been considered. Finally, after the classification of the weighted features using a support vector machine classifier, the performance of the suggested method has been compared with the existing methods based on frequency band selection, on the same BCI competitions datasets. The results show that the proposed method yields superior results on "ay" subject dataset compared against existing approaches such as sub-band CSP, filter bank CSP (FBCSP), discriminative FBCSP, and sliding window discriminative CSP.

No MeSH data available.