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Classification of Two Class Motor Imagery Tasks Using Hybrid GA-PSO Based K-Means Clustering.

- Comput Intell Neurosci (2015)

Bottom Line: Transferring the brain computer interface (BCI) from laboratory condition to meet the real world application needs BCI to be applied asynchronously without any time constraint.Time frequency representation (TFR) techniques have been used to extract the feature of the signal under investigation.TFRs based features are extracted and relying on the concept of event related synchronization (ERD) and desynchronization (ERD) feature vector is formed.

View Article: PubMed Central - PubMed

Affiliation: Electrical and Electronics Engineering, Birla Institute of Technology, Mesra, Ranchi 835215, India.

ABSTRACT
Transferring the brain computer interface (BCI) from laboratory condition to meet the real world application needs BCI to be applied asynchronously without any time constraint. High level of dynamism in the electroencephalogram (EEG) signal reasons us to look toward evolutionary algorithm (EA). Motivated by these two facts, in this work a hybrid GA-PSO based K-means clustering technique has been used to distinguish two class motor imagery (MI) tasks. The proposed hybrid GA-PSO based K-means clustering is found to outperform genetic algorithm (GA) and particle swarm optimization (PSO) based K-means clustering techniques in terms of both accuracy and execution time. The lesser execution time of hybrid GA-PSO technique makes it suitable for real time BCI application. Time frequency representation (TFR) techniques have been used to extract the feature of the signal under investigation. TFRs based features are extracted and relying on the concept of event related synchronization (ERD) and desynchronization (ERD) feature vector is formed.

No MeSH data available.


(a) Variation in misclassification (%) with iterations for GA-PSO based K-means clustering for different subjects. (b) Variation in misclassification (%) with iterations for GA based K-means clustering for different subjects. (c) Variation in misclassification (%) with iterations for PSO based K-means clustering for different subjects.
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fig3: (a) Variation in misclassification (%) with iterations for GA-PSO based K-means clustering for different subjects. (b) Variation in misclassification (%) with iterations for GA based K-means clustering for different subjects. (c) Variation in misclassification (%) with iterations for PSO based K-means clustering for different subjects.

Mentions: Table 2 shows the results for performance of GA based K-means classifier algorithm, PSO based K-means classifier and GA-PSO based K-means classifier algorithm based on accuracy obtained on test set for each of nine subjects. Figure 3 shows the variation of misclassification (%) with number of iterations for an arbitrary subject. It can be seen that the hybrid technique not only achieves a higher classification but also achieves the same with lesser iterations. The lesser execution time of the hybrid technique makes it suitable for real time BCI application, where imagery signal needs to be classified at a very fast rate. Further we used statistical test on the results to test the significance of the result. Table 3 indicates the average ranking of clustering algorithms based on the Friedman's test and Table 4 shows various statistical values from Friedman and Iman-Davenport tests indicating rejection of hypothesis. The rejection of hypothesis indicates similarity in the superiority of the proposed hybrid GA-PSO method over other techniques, across all the subjects.


Classification of Two Class Motor Imagery Tasks Using Hybrid GA-PSO Based K-Means Clustering.

- Comput Intell Neurosci (2015)

(a) Variation in misclassification (%) with iterations for GA-PSO based K-means clustering for different subjects. (b) Variation in misclassification (%) with iterations for GA based K-means clustering for different subjects. (c) Variation in misclassification (%) with iterations for PSO based K-means clustering for different subjects.
© Copyright Policy - open-access
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC4417985&req=5

fig3: (a) Variation in misclassification (%) with iterations for GA-PSO based K-means clustering for different subjects. (b) Variation in misclassification (%) with iterations for GA based K-means clustering for different subjects. (c) Variation in misclassification (%) with iterations for PSO based K-means clustering for different subjects.
Mentions: Table 2 shows the results for performance of GA based K-means classifier algorithm, PSO based K-means classifier and GA-PSO based K-means classifier algorithm based on accuracy obtained on test set for each of nine subjects. Figure 3 shows the variation of misclassification (%) with number of iterations for an arbitrary subject. It can be seen that the hybrid technique not only achieves a higher classification but also achieves the same with lesser iterations. The lesser execution time of the hybrid technique makes it suitable for real time BCI application, where imagery signal needs to be classified at a very fast rate. Further we used statistical test on the results to test the significance of the result. Table 3 indicates the average ranking of clustering algorithms based on the Friedman's test and Table 4 shows various statistical values from Friedman and Iman-Davenport tests indicating rejection of hypothesis. The rejection of hypothesis indicates similarity in the superiority of the proposed hybrid GA-PSO method over other techniques, across all the subjects.

Bottom Line: Transferring the brain computer interface (BCI) from laboratory condition to meet the real world application needs BCI to be applied asynchronously without any time constraint.Time frequency representation (TFR) techniques have been used to extract the feature of the signal under investigation.TFRs based features are extracted and relying on the concept of event related synchronization (ERD) and desynchronization (ERD) feature vector is formed.

View Article: PubMed Central - PubMed

Affiliation: Electrical and Electronics Engineering, Birla Institute of Technology, Mesra, Ranchi 835215, India.

ABSTRACT
Transferring the brain computer interface (BCI) from laboratory condition to meet the real world application needs BCI to be applied asynchronously without any time constraint. High level of dynamism in the electroencephalogram (EEG) signal reasons us to look toward evolutionary algorithm (EA). Motivated by these two facts, in this work a hybrid GA-PSO based K-means clustering technique has been used to distinguish two class motor imagery (MI) tasks. The proposed hybrid GA-PSO based K-means clustering is found to outperform genetic algorithm (GA) and particle swarm optimization (PSO) based K-means clustering techniques in terms of both accuracy and execution time. The lesser execution time of hybrid GA-PSO technique makes it suitable for real time BCI application. Time frequency representation (TFR) techniques have been used to extract the feature of the signal under investigation. TFRs based features are extracted and relying on the concept of event related synchronization (ERD) and desynchronization (ERD) feature vector is formed.

No MeSH data available.