title: Classification of Two Class Motor Imagery Tasks Using Hybrid GA-PSO Based K-Means Clustering.
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.
authors.affiliation: Electrical and Electronics Engineering, Birla Institute of Technology, Mesra, Ranchi 835215, India.
authors.lastname: Suraj
authors.affiliation: M. Tech., Computer Technology, National Institute of Technology, Raipur 492001, India.
authors.forename: Purnendu
authors.initials: P
authors.lastname: Tiwari
authors.affiliation: Electrical and Electronics Engineering, National Institute of Technology, Raipur 492001, India.
authors.forename: Subhojit
authors.initials: S
authors.lastname: Ghosh
authors.affiliation: Birla Institute of Technology, Mesra, Ranchi 835215, India.
authors.forename: Rakesh Kumar
authors.initials: RK
authors.lastname: Sinha
mesh_terms: D000328:Adult; D000465:Algorithms; D001921:Brain / Q000502:physiology*; D058256:Brain Waves / Q000502:physiology*; D062207:Brain-Computer Interfaces; D016000:Cluster Analysis; D016002:Discriminant Analysis; D004569:Electroencephalography; D005260:Female; D006801:Humans; D007092:Imagination; D008297:Male; D009068:Movement / Q000502:physiology*; D012815:Signal Processing, Computer-Assisted; D014584:User-Computer Interface; D055815:Young Adult