Limits...
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) Born Jordan feature for ß band in C3 channel for arbitrary trial corresponding to right task. (b) Born Jordan feature for ß band in C4 channel for arbitrary trial corresponding to right task. (c) Born Jordan feature for µ band in C4 channel for arbitrary trial corresponding to left task. (d) Born Jordan feature for µ band in C4 channel for arbitrary trial corresponding to right task.
© Copyright Policy - open-access
Related In: Results  -  Collection


getmorefigures.php?uid=PMC4417985&req=5

fig2: (a) Born Jordan feature for ß band in C3 channel for arbitrary trial corresponding to right task. (b) Born Jordan feature for ß band in C4 channel for arbitrary trial corresponding to right task. (c) Born Jordan feature for µ band in C4 channel for arbitrary trial corresponding to left task. (d) Born Jordan feature for µ band in C4 channel for arbitrary trial corresponding to right task.

Mentions: The TFR features used in classification of MI based tasks in this experiment are listed in Table 1. After computing different features in order to form feature vector, absolute value of features is computed. For extracting features, a data vector of length 1.25 second is taken with sampling frequency of 500 Hz. This is also chosen as epoch length and for every processing, a signal vector of 625 points is used at a time. We have used data corresponding to C3 and C4 channels for task discrimination as used in [29]. Figure 2 shows Born Jordan feature computed for arbitrary trials. These features have been computed using time frequency toolbox of MATLAB [24]. Maximum value of the absolute value of feature value (TFRs) obtained for every epoch of data for various features is determined. Then mean value is computed for every trial of each type of tasks. Further concept of ERS and ERD is used in forming feature vector as mentioned in [15, 26] which states that there is ERS of the μ rhythm on the contralateral side and a slight ERS in the central ß rhythm on the ipsilateral hemisphere. This hemispheric asymmetry reflected in the EEGs is exploited to differentiate the task desirably. To exploit the concept of hemispheric asymmetry with regard to μ and ß bands, we combined feature matrix obtained with respect to μ and ß bands to form a pattern, which is likely to inherit the property of ERS and ERD as mentioned in [15]. This resulted in a feature matrix of dimension 160 × 22 where there are twenty-two features using two bands of signal with eleven different features for eighty each of the two classes for every session. In our present work we used two sessions at a time for purpose of classifications. Thus a composite feature matrix of dimension 320 × 22 is obtained. Different techniques of TFR are extensively used in the area of BCI [15, 30–39].


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

- Comput Intell Neurosci (2015)

(a) Born Jordan feature for ß band in C3 channel for arbitrary trial corresponding to right task. (b) Born Jordan feature for ß band in C4 channel for arbitrary trial corresponding to right task. (c) Born Jordan feature for µ band in C4 channel for arbitrary trial corresponding to left task. (d) Born Jordan feature for µ band in C4 channel for arbitrary trial corresponding to right task.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

fig2: (a) Born Jordan feature for ß band in C3 channel for arbitrary trial corresponding to right task. (b) Born Jordan feature for ß band in C4 channel for arbitrary trial corresponding to right task. (c) Born Jordan feature for µ band in C4 channel for arbitrary trial corresponding to left task. (d) Born Jordan feature for µ band in C4 channel for arbitrary trial corresponding to right task.
Mentions: The TFR features used in classification of MI based tasks in this experiment are listed in Table 1. After computing different features in order to form feature vector, absolute value of features is computed. For extracting features, a data vector of length 1.25 second is taken with sampling frequency of 500 Hz. This is also chosen as epoch length and for every processing, a signal vector of 625 points is used at a time. We have used data corresponding to C3 and C4 channels for task discrimination as used in [29]. Figure 2 shows Born Jordan feature computed for arbitrary trials. These features have been computed using time frequency toolbox of MATLAB [24]. Maximum value of the absolute value of feature value (TFRs) obtained for every epoch of data for various features is determined. Then mean value is computed for every trial of each type of tasks. Further concept of ERS and ERD is used in forming feature vector as mentioned in [15, 26] which states that there is ERS of the μ rhythm on the contralateral side and a slight ERS in the central ß rhythm on the ipsilateral hemisphere. This hemispheric asymmetry reflected in the EEGs is exploited to differentiate the task desirably. To exploit the concept of hemispheric asymmetry with regard to μ and ß bands, we combined feature matrix obtained with respect to μ and ß bands to form a pattern, which is likely to inherit the property of ERS and ERD as mentioned in [15]. This resulted in a feature matrix of dimension 160 × 22 where there are twenty-two features using two bands of signal with eleven different features for eighty each of the two classes for every session. In our present work we used two sessions at a time for purpose of classifications. Thus a composite feature matrix of dimension 320 × 22 is obtained. Different techniques of TFR are extensively used in the area of BCI [15, 30–39].

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.